-DOCSTART- -X- O O 0b67946e55e9780a2939fcfccbcfb167

The O
outcomes O
of O
the O
sensitivity O
analysis O
framework O
suggest O
that O
flood B-climate-hazards
risk O
can O
vary O
dramatically O
as O
a O
result O
of O
possible O
change O
scenarios O
. O

The O
risk O
components O
that O
have O
not O
received O
much O
attention O
( O
e.g. O
changes O
in O
dike B-climate-mitigations
systems O
and O
in O
vulnerability O
) O
may O
mask O
the O
influence O
of O
climate O
change O
that O
is O
often O
investigated O
component O
. O

-DOCSTART- -X- O O b270c8b103e19e6a09774c222e885aff

A O
parameterization O
of O
vertical B-climate-properties
diffusivity I-climate-properties
in O
ocean B-climate-nature
general O
circulation O
models O
has O
been O
implemented O
in O
the O
ocean B-climate-nature
model O
component O
of O
the O
Community B-climate-models
Climate I-climate-models
System I-climate-models
Model I-climate-models
( O
CCSM B-climate-models
) O
. O

The O
parameterization O
represents O
the O
dynamics O
of O
the O
mixing O
in O
the O
abyssal B-climate-nature
ocean I-climate-nature
arising O
from O
the O
breaking O
of O
internal B-climate-nature
waves I-climate-nature
generated O
by O
the O
tides B-climate-nature
forcing O
stratified O
flow O
over O
rough O
topography B-climate-nature
. O

Diapycnal B-climate-nature
mixing I-climate-nature
in O
the O
ocean B-climate-nature
is O
thought O
to O
be O
one O
of O
the O
primary O
controls O
on O
the O
meridional B-climate-nature
overturning I-climate-nature
circulation I-climate-nature
and O
the O
poleward B-climate-nature
heat I-climate-nature
transport I-climate-nature
by O
the O
ocean B-climate-nature
. O

The O
poleward B-climate-nature
ocean I-climate-nature
heat I-climate-nature
transport I-climate-nature
does O
not O
appear O
to O
be O
strongly O
affected O
by O
the O
mixing O
in O
the O
abyssal B-climate-nature
ocean I-climate-nature
for O
reasonable O
parameter O
ranges O
. O

The O
transport O
of O
the O
Antarctic B-climate-nature
Circumpolar I-climate-nature
Current I-climate-nature
through O
the O
Drake O
Passage O
is O
related O
to O
the O
amount O
of O
mixing O
in O
the O
deep B-climate-nature
ocean I-climate-nature
. O

-DOCSTART- -X- O O 3c8e05c983415b10eb34adecaffdc75f

A O
wide O
variety O
of O
aerosols B-climate-nature
are O
suspended O
in O
the O
atmosphere B-climate-nature
. O

Especially O
in O
East O
Asia O
, O
a O
huge O
amount O
of O
fossil B-climate-problem-origins
fuel I-climate-problem-origins
burning O
aerosols B-climate-nature
are O
emitted O
throughout O
the O
year O
. O

Further O
it O
seems O
that O
the O
characteristics O
of O
aerosols B-climate-nature
change O
with O
the O
season O
, O
and O
hence O
the O
influence O
impact O
of O
aerosols B-climate-nature
over O
the O
climate O
also O
varies O
according O
to O
the O
season O
. O

This O
work O
is O
based O
on O
the O
Moderate B-climate-observations
- I-climate-observations
resolution I-climate-observations
Imaging I-climate-observations
Spectroradiometer I-climate-observations
( O
MODIS B-climate-observations
) O
data O
and O
the O
simulation O
results O
with O
a O
three O
- O
dimensional O
aerosol B-climate-nature
transport O
- O
radiation O
model O
. O

This O
result O
is O
drawn O
from O
both O
MODIS B-climate-observations
data O
and O
model O
simulations O
. O

MODIS B-climate-observations
data O
shows O
that O
the O
sulfate O
aerosols B-climate-nature
are O
much O
more O
dominant O
in O
summer O
than O
those O
in O
winter O
at O
Beijing O
which O
is O
influenced O
strongly O
by O
fossil B-climate-problem-origins
fuel I-climate-problem-origins
burning O
aerosols B-climate-nature
. O

From O
the O
present O
study O
I O
can O
draw O
such O
a O
result O
in O
respect O
of O
surface B-climate-properties
radiation I-climate-properties
budget O
as O
the O
aerosol B-climate-nature
impact O
on O
the O
reduction O
of O
solar B-climate-properties
radiation I-climate-properties
is O
more O
dominant O
in O
summer O
than O
that O
in O
winter O
in O
East O
Asia O
. O

-DOCSTART- -X- O O 8064c445c2ddb2e7fc62737ce8ef5757

The O
latest O
changes O
of O
Earth O
's O
climate O
significantly O
affect O
human O
ability O
to O
remain O
food B-climate-assets
production O
sustainable O
. O

Expected O
climate O
changes O
will O
influence O
environmental O
conditions O
suitable O
for O
diseases B-climate-impacts
and O
pests B-climate-hazards
appearance O
. O

Results O
obtained O
in O
this O
assessment O
study O
indicate O
that O
, O
meteorological O
conditions O
which O
can O
be O
expected O
in O
Serbia O
in O
2030 O
and O
2050 O
according O
to O
ECHAM B-climate-models
climate O
model O
will O
significantly O
affect O
time O
of O
appearance O
and O
probability O
of O
attack O
of O
plasmopara B-climate-hazards
viticola I-climate-hazards
which O
is O
causal O
agent O
of O
downy B-climate-impacts
mildew I-climate-impacts
of O
grapevine B-climate-assets
. O

-DOCSTART- -X- O O 9341b2d7b00dc274595420b4d2635eb7

Atmospheric B-climate-nature
aerosols I-climate-nature
influence O
energy O
balance O
of O
the O
Arctic O
region O
via O
scattering O
solar B-climate-properties
radiation I-climate-properties
, O
influencing O
albedo B-climate-properties
and O
lifetime B-climate-properties
of O
clouds B-climate-nature
, O
and O
via O
deposition O
on O
snow B-climate-nature
and O
ice B-climate-nature
surfaces I-climate-nature
. O

To O
know O
about O
the O
impact O
of O
arctic O
aerosols B-climate-nature
is O
crucial O
, O
because O
the O
Arctic O
region O
is O
a O
very O
sensitive O
area O
and O
an O
important O
factor O
in O
global O
circulation O
and O
climate O
. O

State O
- O
of O
- O
the O
art O
algorithms O
are O
not O
able O
to O
resolve O
this O
task O
, O
leading O
to O
gaps O
in O
aerosol B-climate-nature
data O
in O
the O
Polar O
regions O
. O

The O
algorithm O
is O
a O
multi O
- O
angle O
approach O
, O
based O
on O
the O
dual O
- O
viewing O
Advanced B-climate-observations
Along I-climate-observations
- I-climate-observations
Track I-climate-observations
Scanning I-climate-observations
Radiometer I-climate-observations
( O
AATSR B-climate-observations
) O
onboard O
ENVISAT B-climate-observations
. O

A O
number O
of O
conducted O
simulation O
and O
modeling O
studies O
preceded O
the O
establishment O
of O
automatic O
snow B-climate-nature
/ O
cloud B-climate-nature
discrimination O
algorithm O
and O
aerosol B-climate-nature
optical B-climate-properties
thickness I-climate-properties
retrievals O
in O
the O
visible O
and O
infrared O
spectral O
regions O
. O

-DOCSTART- -X- O O bab0dca0eaf5dab9ed0911bfc8f79667

In O
this O
study O
, O
we O
measure O
pH B-climate-properties
levels I-climate-properties
in O
a O
transect O
of O
the O
Kuroshio O
Extension O
, O
a O
turbulent O
ocean B-climate-nature
region O
that O
is O
known O
for O
high O
CO2 B-climate-greenhouse-gases
drawdown O
in O
late O
winter O
, O
and O
therefore O
a O
potential O
site O
for O
intense O
spatial O
and O
temporal O
pH B-climate-properties
changes I-climate-properties
. O

We O
provide O
baseline O
pH B-climate-properties
levels I-climate-properties
down O
to O
2000 O
m O
across O
the O
transect O
using O
the O
spectrophotometric O
procedure O
and O
find O
that O
surface B-climate-properties
pH I-climate-properties
decreases O
0.37 O
pH O
units O
as O
we O
progressed O
north O
along O
the O
cruise O
track O
( O
30 O
- O
41 O
° O
N O
) O
. O

We O
compare O
this O
method O
of O
acquiring O
pH B-climate-properties
with O
the O
method O
of O
calculating O
pH B-climate-properties
from O
alkalinity B-climate-properties
and O
dissolved O
inorganic O
carbon O
. O

Lastly O
, O
we O
compare O
current O
calculated O
pH B-climate-properties
to O
data O
from O
previous O
cruises O
( O
WOCE B-climate-organizations
1993 O
and O
CLIVAR B-climate-organizations
2007 O
) O
to O
assess O
pH B-climate-properties
changes I-climate-properties
with O
time O
. O

The O
problem O
of O
ocean B-climate-hazards
acidification I-climate-hazards
is O
a O
fairly O
recent O
one O
, O
a O
direct O
result O
of O
human O
activities O
in O
the O
industrial O
age O
. O

This O
increase O
in O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
has O
dire O
consequences O
for O
the O
world O
’s O
oceans B-climate-nature
, O
which O
act O
as O
2 O
the O
largest O
sink O
for O
atmospheric B-climate-nature
CO2 B-climate-greenhouse-gases
. O

-DOCSTART- -X- O O 3275359b287ae82f0a525d99cb008b07

Sedimentary O
characteristics O
of O
channel B-climate-nature
fills I-climate-nature
and O
flood B-climate-nature
beds I-climate-nature
were O
correlated O
with O
contemporaneous O
discharge B-climate-nature
records O
( O
since O
1770 O
AD O
) O
and O
major O
geomorphological O
changes O
in O
the O
Lower O
Rhine O
floodplain B-climate-nature
. O

Discharges O
of O
palaeofloods B-climate-hazards
were O
calculated O
from O
the O
established O
regression O
between O
grain O
- O
size O
characteristics O
in O
flood B-climate-hazards
deposits O
and O
measured O
discharges O
, O
and O
hydraulic O
modelling O
based O
on O
the O
elevation O
of O
slackwater B-climate-nature
deposits I-climate-nature
on O
high O
terrace B-climate-nature
levels O
in O
the O
Lower O
Rhine O
Valley O
. O

It O
was O
found O
that O
around O
4700 O
years O
ago O
, O
an O
extreme O
must O
have O
occurred O
of O
at O
least O
13,250 O
m3s-1 O
, O
larger O
than O
any O
measured O
discharge B-climate-properties
. O

Considering O
anthropogenic O
adjustments O
, O
this O
discharge B-climate-properties
corresponds O
to O
at O
least O
14,000 O
m3s-1 O
in O
the O
present O
situation O
, O
thus O
reaching O
similar O
values O
as O
current O
flood B-climate-hazards
protection O
levels O
. O

Other O
floods B-climate-hazards
of O
similar O
size O
( O
millennium O
floods O
) O
occurred O
around O
784 O
and O
1374 O
AD O
, O
and O
4500 O
and O
6200 O
years O
ago O
. O

-DOCSTART- -X- O O 9ad1062de7cd3528d4b18c09d250af7f

Climate O
change O
threatens O
global O
ecosystems O
and O
the O
maintenance O
of O
biodiversity B-climate-organisms
via O
its O
impacts O
on O
the O
survival B-climate-organisms
of I-climate-organisms
individual I-climate-organisms
species I-climate-organisms
and O
the O
preservation O
of O
their O
ecological O
functions O
. O

The O
effects O
of O
climate O
change O
are O
particularly O
evident O
in O
the O
mountainous B-climate-nature
areas O
of O
southwestern O
China O
that O
support O
the O
last O
remaining O
populations O
of O
giant B-climate-organisms
pandas I-climate-organisms
( O
Ailuropoda B-climate-organisms
melanoleuca I-climate-organisms
) O
. O

In O
this O
paper O
, O
we O
developed O
a O
mechanistic O
model O
that O
uses O
climatic O
variables O
( O
rather O
than O
biotic O
variables O
) O
to O
( O
i O
) O
examine O
how O
variation O
in O
landscape O
scale O
climate O
influences O
the O
spatial O
distribution O
of O
panda B-climate-organisms
habitat O
in O
China O
's O
Qinling O
Mountains O
, O
and O
( O
ii O
) O
evaluate O
how O
the O
distribution O
and O
extent O
of O
panda B-climate-organisms
habitat B-climate-organisms
will O
change O
in O
the O
future O
under O
forecast O
climate O
change O
scenarios O
. O

We O
found O
that O
there O
was O
substantial O
variation O
in O
temperature B-climate-properties
throughout O
the O
study O
area O
that O
correlated O
with O
variation O
in O
altitude B-climate-properties
. O

Our O
model O
results O
revealed O
that O
this O
predicted O
climate O
change O
could O
reduce O
the O
extent O
of O
a O
suitable O
habitat O
for O
giant B-climate-organisms
pandas I-climate-organisms
by O
up O
to O
62 O
% O
( O
under O
IPCC B-climate-organizations
SRES B-climate-datasets
A2 I-climate-datasets
scenarios O
; O
and O
37 O
% O
under O
IPCC B-climate-organizations
SRES B-climate-datasets
B2 I-climate-datasets
scenarios O
) O
. O

-DOCSTART- -X- O O a295078f98f657aa156c4d9c5719f43f

The O
purpose O
of O
this O
work O
is O
to O
assess O
the O
sugarcane B-climate-assets
yield O
variation O
in O
regional O
scale O
through O
NDVI B-climate-observations
images O
from O
a O
low O
resolution O
spatial O
satellite O
. O

We O
have O
used O
Principal O
Component O
Analysis O
( O
PCA O
) O
and O
Cluster O
Analysis O
to O
correlate O
sugarcane B-climate-assets
cultivated B-climate-assets
land I-climate-assets
with O
multitemporal O
NDVI B-climate-observations
images O
also O
verifying O
the O
influence O
of O
climate O
conditions O
to O
them O
. O

-DOCSTART- -X- O O 7db31c01274a29af6747ed637d1be1df

Such O
a O
slight O
change O
in O
the O
thermohaline B-climate-nature
circulation I-climate-nature
does O
not O
have O
any O
significant O
impact O
on O
the O
heat O
balance O
of O
the O
northern O
high O
latitudes O
between O
126 O
and O
115 O
kyr O
BP O
. O

-DOCSTART- -X- O O 2208f924c9e839b5bdd66b167bb5d587

Great O
Lakes B-climate-nature
are O
facing O
an O
elevated O
nutrients B-climate-problem-origins
discharges B-climate-problem-origins
from O
agricultural B-climate-assets
watersheds B-climate-nature
that O
could O
lead O
to O
the O
lakes B-climate-hazards
impairment I-climate-hazards
. O

Non B-climate-hazards
- I-climate-hazards
point I-climate-hazards
source I-climate-hazards
( O
NPS B-climate-hazards
) O
pollution B-climate-hazards
is O
the O
leading O
contributor O
of O
the O
lakes B-climate-hazards
impairment I-climate-hazards
. O

Policy O
makers O
are O
required O
to O
establish O
guidelines O
and O
polices O
that O
governs O
and O
distribute O
agri O
- O
environmental O
funds O
to O
minimize O
the O
agricultural B-climate-hazards
NPS I-climate-hazards
pollutions I-climate-hazards
. O

The O
Soil B-climate-models
and I-climate-models
Water I-climate-models
Assessment I-climate-models
Tool I-climate-models
( O
SWAT B-climate-models
) O
is O
the O
most O
used O
model O
to O
simulate O
watershed B-climate-nature
and O
BMPs B-climate-mitigations
scenarios O
. O

-DOCSTART- -X- O O 2ff9927985b0bccf130dbd23627389bf

The O
objective O
of O
this O
study O
is O
to O
examine O
UHS B-climate-properties
as O
an O
Urban B-climate-hazards
Heat I-climate-hazards
Hazard I-climate-hazards
( O
UHH B-climate-hazards
) O
based O
on O
Universal B-climate-datasets
Temperature I-climate-datasets
Climate I-climate-datasets
Index I-climate-datasets
( O
UTCI B-climate-datasets
) O
and O
Effective B-climate-datasets
Temperature I-climate-datasets
Index I-climate-datasets
( O
ETI B-climate-datasets
) O
in O
University O
of O
Indonesia O
. O

Thermal O
bands O
of O
Landsat B-climate-observations
8 I-climate-observations
images O
( O
the O
acquisition O
year O
2013 O
- O
2015 O
) O
were O
used O
to O
create O
LST B-climate-properties
model O
. O

The O
UTCI B-climate-datasets
showed O
“ O
moderate O
” O
and O
“ O
strong O
heat B-climate-hazards
stress I-climate-hazards
” O
, O
while O
EFI B-climate-datasets
showed O
“ O
uncomfortable O
” O
and O
“ O
very O
uncomfortable O
” O
categories O
during O
that O
period O
. O

Range O
UHS B-climate-properties
in O
Campus O
UI O
on O
2013 O
( O
21.8 O
- O
31.1 O
o O
C O
) O
, O
2014 O
( O
25.0 O
- O
36.2 O
o O
C O
) O
and O
2015 O
( O
24.9 O
- O
38.2 O
o O
C O
) O
. O

This O
maximum O
UHS B-climate-properties
on O
September O
( O
2014 O
and O
2015 O
) O
put O
on O
levelling O
UTCI B-climate-datasets
included O
range O
temperature B-climate-properties
32 O
- O
35 O
o O
C O
, O
with O
an O
explanation O
of O
sensation B-climate-properties
temperature I-climate-properties
is O
warm O
and O
sensation O
of O
comfort O
is O
Uncomfortable O
, O
Psychology O
with O
Increasing O
Stress O
Case O
by O
Sweating O
and O
Blood O
Flow O
and O
Health B-climate-assets
category O
is O
Cardiovascular B-climate-hazards
Embarrassment I-climate-hazards
. O

-DOCSTART- -X- O O 6a6478c27a4bac4b79fedb342f3d83f7

Land B-climate-nature
- I-climate-nature
sea I-climate-nature
riverine I-climate-nature
carbon I-climate-nature
transfer I-climate-nature
( O
LSRCT B-climate-nature
) O
is O
one O
of O
the O
key O
processes O
in O
the O
global B-climate-nature
carbon I-climate-nature
cycle I-climate-nature
. O

This O
study O
presents O
an O
integrated O
framework O
coupling O
hydrological B-climate-nature
modeling O
, O
field O
sampling O
and O
stable O
isotope O
analysis O
for O
the O
quantitative O
assessment O
of O
the O
impact O
of O
human B-climate-mitigations
water I-climate-mitigations
management I-climate-mitigations
practices I-climate-mitigations
( O
e.g. O
irrigation B-climate-mitigations
, O
dam B-climate-mitigations
construction I-climate-mitigations
) O
on O
LSRCT B-climate-nature
under O
different O
hydrological B-climate-nature
conditions O
. O

By O
applying O
this O
approach O
to O
the O
case O
study O
of O
the O
Nandu O
River O
, O
China O
, O
we O
find O
that O
carbon B-climate-properties
( I-climate-properties
C I-climate-properties
) I-climate-properties
concentrations I-climate-properties
originating O
from O
different O
land O
- O
uses O
( O
e.g. O
forest B-climate-nature
, O
cropland B-climate-assets
) O
are O
relatively O
stable O
and O
outlet O
C O
variations O
are O
mainly O
dominated O
by O
controlled O
runoff B-climate-properties
volumes I-climate-properties
rather O
than O
by O
input B-climate-properties
C I-climate-properties
concentrations I-climate-properties
. O

In O
addition O
, O
isotope O
analysis O
also O
shows O
that O
C B-climate-nature
fluxes I-climate-nature
influenced O
by O
human O
activities O
( O
e.g. O
agriculture B-climate-assets
, O
aquaculture B-climate-assets
) O
could O
contribute O
the O
dominant O
particulate O
organic O
carbon O
under O
typical O
climatic O
conditions O
, O
as O
well O
as O
drought B-climate-hazards
conditions O
. O

-DOCSTART- -X- O O f8bbb24ee89032167e03047715d60657

Much O
of O
the O
debate O
on O
global O
climate O
change O
has O
focused O
on O
direct O
costs O
of O
mitigation O
. O

The O
present O
analysis O
incorporates O
a O
linkage O
between O
air B-climate-hazards
pollution I-climate-hazards
and O
ancillary B-climate-assets
health I-climate-assets
benefits I-climate-assets
into O
a O
general O
equilibrium O
model O
applied O
to O
Sweden O
. O

Health B-climate-assets
benefits I-climate-assets
are O
compared O
in O
three O
scenarios O
for O
attaining O
the O
Swedish O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
target O
with O
alternative O
projected O
and O
harmful O
emission O
levels O
. O

Results O
show O
that O
the O
costs O
of O
climate B-climate-mitigations
policy I-climate-mitigations
could O
be O
overstated O
when O
not O
accounting O
for O
ancillary O
health B-climate-assets
benefits I-climate-assets
. O

-DOCSTART- -X- O O 9a2a258aaf9b6e6f73aacc27bb235bff

Concerns O
have O
been O
raised O
about O
the O
recent O
arrival O
of O
Mikania B-climate-hazards
micrantha I-climate-hazards
Kunth I-climate-hazards
in O
south O
Florida O
and O
its O
potential O
to O
spread O
and O
invade O
natural O
and O
managed O
ecosystems O
. O

This O
weed B-climate-hazards
is O
native O
to O
the O
neotropics O
, O
and O
has O
been O
introduced O
into O
many O
Asian O
countries O
and O
, O
more O
recently O
, O
into O
Australia O
. O

In O
Asia O
, O
M. B-climate-hazards
micrantha I-climate-hazards
is O
particularly O
problematic O
in O
plantation B-climate-assets
crops I-climate-assets
, O
but O
also O
threatens O
natural O
areas O
and O
disturbed B-climate-hazards
ecosystems I-climate-hazards
. O

Previous O
biological B-climate-mitigations
control I-climate-mitigations
programs I-climate-mitigations
provide O
valuable O
information O
for O
Florida O
scientists O
about O
the O
availability O
of O
natural B-climate-mitigations
enemies I-climate-mitigations
and O
potential O
areas O
for O
future O
foreign O
surveys O
. O

-DOCSTART- -X- O O 3efb679b7455e038d3a64e730e566227

Quantifying O
the O
effect O
of O
the O
seawater B-climate-properties
density I-climate-properties
changes I-climate-properties
on O
sea B-climate-properties
level I-climate-properties
variability I-climate-properties
is O
of O
crucial O
importance O
for O
climate O
change O
studies O
, O
as O
the O
sea B-climate-properties
level I-climate-properties
cumulative I-climate-properties
rise I-climate-properties
can O
be O
regarded O
as O
both O
an O
important O
climate O
change O
indicator O
and O
a O
possible O
danger O
for O
human O
activities O
in O
coastal B-climate-nature
areas I-climate-nature
. O

-DOCSTART- -X- O O 6b7d27cb337c5cb2fcef67166a04252f

In O
this O
work O
, O
as O
part O
of O
the O
Ocean B-climate-nature
Reanalysis I-climate-models
Intercomparison I-climate-models
Project I-climate-models
, O
the O
global O
and O
regional O
steric B-climate-properties
sea I-climate-properties
level I-climate-properties
changes I-climate-properties
are O
estimated O
and O
compared O
from O
an O
ensemble O
of O
16 O
ocean B-climate-nature
reanalyses O
and O
4 O
objective O
analyses O
. O

The O
ensemble O
mean O
exhibits O
a O
significant O
high O
correlation O
at O
both O
global O
and O
regional O
scale O
, O
and O
the O
ensemble O
of O
ocean B-climate-nature
reanalyses O
outperforms O
that O
of O
objective O
analyses O
, O
in O
particular O
in O
the O
Southern O
Ocean O
. O

Furthermore O
, O
the O
impact O
of O
deep B-climate-nature
ocean I-climate-nature
layers I-climate-nature
is O
non O
- O
negligible O
on O
the O
steric B-climate-properties
sea I-climate-properties
level I-climate-properties
variability I-climate-properties
( O
22 O
and O
12 O
% O
for O
the O
layers O
below O
700 O
and O
1500 O
m O
of O
depth B-climate-properties
, O
respectively O
) O
, O
although O
the O
small O
deep B-climate-nature
ocean I-climate-nature
trends O
are O
not O
significant O
with O
respect O
to O
the O
products O
spread O
. O

-DOCSTART- -X- O O ee87c5adfc8eee20b43508491ba85137

Recently O
, O
Wadi B-climate-hazards
flash I-climate-hazards
floods I-climate-hazards
( O
WFFs B-climate-hazards
) O
have O
happened O
frequently O
in O
arid B-climate-nature
environments I-climate-nature
, O
resulting O
in O
great O
damage B-climate-impacts
the O
society O
and O
the O
environment O
. O

In O
Oman O
, O
severe O
WFFs B-climate-hazards
have O
occurred O
repeatedly O
within O
the O
last O
10 O
years O
causing O
a O
huge O
impact O
on O
human B-climate-assets
lives I-climate-assets
and O
properties O
. O

This O
paper O
aims O
at O
introducing O
the O
framework O
of O
an O
international O
collaboration O
project O
between O
Japan O
and O
Oman O
for O
WFF B-climate-mitigations
management I-climate-mitigations
considering O
sediment B-climate-nature
dynamics I-climate-nature
and O
climate O
changes O
. O

The O
detailed O
field O
survey O
to O
assess O
the O
deposited B-climate-nature
sediment I-climate-nature
in O
a O
dry O
reservoir B-climate-mitigations
by O
using O
sediment O
bars O
, O
and O
infiltration B-climate-properties
test O
, O
as O
well O
as O
drone O
survey O
were O
addressed O
. O

Based O
on O
the O
historical O
rainfall B-climate-nature
data O
analysis O
, O
there O
is O
a O
systematic O
increasing O
trend O
of O
the O
annual B-climate-properties
average I-climate-properties
precipitation I-climate-properties
with O
remarkable O
cycles O
over O
the O
MENA O
region O
and O
Oman O
. O

The O
knowledge O
obtained O
from O
this O
project O
is O
expected O
to O
be O
valuable O
to O
understanding O
sediment B-climate-nature
dynamics I-climate-nature
at O
Wadi O
basins O
. O

-DOCSTART- -X- O O a6f2ce346e5cf3ef19c9d1ac051c6b37

The O
most O
important O
decision O
about O
slurry B-climate-problem-origins
spreading I-climate-problem-origins
concerns O
the O
selection O
of O
spreading O
date O
and O
the O
selection O
of O
fields O
which O
are O
likely O
to O
produce O
only O
moderate O
leaching B-climate-impacts
effects I-climate-impacts
. O

Climatic O
variation O
as O
exemplified O
in O
the O
three O
meteorological B-climate-nature
data O
sets O
, O
produces O
noticeable O
and O
significant O
differences O
in O
both O
N B-climate-impacts
leached I-climate-impacts
and O
harvest B-climate-assets
crop I-climate-assets
totals I-climate-assets
. O

This O
study O
also O
aims O
to O
identify O
that O
a O
field O
environmental O
risk O
assessment O
( O
ERA O
) O
using O
a O
physically O
based O
model O
such O
as O
SOILN B-climate-models
can O
be O
determined O
such O
that O
strategic O
agronomic O
decisions O
involving O
N O
management O
can O
be O
made O
. O

-DOCSTART- -X- O O e98d546cc1e398014179f8b7e088d49f

Abstract O
Rainwater B-climate-mitigations
harvesting I-climate-mitigations
( O
RWH B-climate-mitigations
) O
is O
the O
collection O
and O
storage B-climate-mitigations
of I-climate-mitigations
runoff I-climate-mitigations
for O
the O
primary O
purpose O
of O
groundwater B-climate-properties
recharge I-climate-properties
in O
arid O
and O
semi O
- O
arid O
regions O
of O
India O
. O

In O
India O
, O
investment O
in O
RWH B-climate-mitigations
for O
groundwater B-climate-properties
recharge I-climate-properties
is O
increasing O
. O

This O
paper O
therefore O
proposes O
a O
method O
to O
explore O
the O
effects O
of O
RWH B-climate-nature
in O
a O
case O
study O
catchment B-climate-nature
of O
the O
500 O
km2 O
ungauged O
Arvari O
River O
Basin O
in O
Rajasthan O
, O
India O
, O
where O
366 O
RWH B-climate-nature
structures O
have O
been O
built O
since O
1985 O
. O

Discharge B-climate-properties
over O
anicuts B-climate-mitigations
on O
the O
river B-climate-nature
was O
also O
monitored O
and O
subsequent O
water B-climate-properties
level I-climate-properties
rise I-climate-properties
in O
wells B-climate-assets
. O

The O
groundwater B-climate-nature
system O
consists O
of O
an O
unconfined B-climate-nature
alluvial I-climate-nature
aquifer I-climate-nature
over O
a O
confined B-climate-nature
hard I-climate-nature
rock I-climate-nature
aquifer I-climate-nature
. O

-DOCSTART- -X- O O 7b554c100b84842cbd39a0cfeb48db1f

Certainly O
land B-climate-problem-origins
cover I-climate-problem-origins
land B-climate-problem-origins
use I-climate-problem-origins
( O
LCLU B-climate-problem-origins
) O
changes O
have O
huge O
impacts O
on O
countries O
overall O
ecological O
balance O
and O
climate O
change O
. O

The O
present O
study O
is O
an O
attempt O
: O
( O
1 O
) O
to O
examine O
the O
land B-climate-problem-origins
use I-climate-problem-origins
change I-climate-problem-origins
drivers O
active O
at O
the O
studied O
landscape O
of O
coastal O
Karnataka O
in O
India O
and O
( O
2 O
) O
to O
model O
the O
LCLU B-climate-properties
changes I-climate-properties
in O
pre O
- O
industrialized O
period O
using O
Dyna B-climate-models
- I-climate-models
CLUE I-climate-models
model O
. O

Binary O
logistic O
regression O
was O
used O
to O
categorize O
land O
change O
drivers O
and O
to O
estimate O
the O
probability O
of O
changes O
. O

They O
being O
slope B-climate-properties
, O
relative O
relief B-climate-properties
, O
drainage B-climate-properties
density I-climate-properties
and O
availability O
of O
ground B-climate-nature
water I-climate-nature
are O
the O
most O
influential O
drivers O
for O
most O
of O
the O
land O
classes O
. O

-DOCSTART- -X- O O 6bbab0f8ad3b8ae7e175b45577ecd597

Future O
changes O
of O
water B-climate-nature
budget I-climate-nature
of O
the O
Upper O
Volga O
catchment O
area O
were O
estimated O
from O
: O
past O
and O
present O
dynamics O
of O
the O
atmospheric B-climate-nature
, O
water B-climate-nature
and O
forest B-climate-nature
conditions O
, O
different O
climatic O
scenarios O
and O
SVAT B-climate-models
( O
Soil B-climate-models
– I-climate-models
Vegetation I-climate-models
– I-climate-models
Atmosphere I-climate-models
Transfer I-climate-models
) O
and O
hydrological B-climate-nature
models O
. O

During O
the O
last O
50–60 O
years O
the O
mean B-climate-properties
annual I-climate-properties
air I-climate-properties
temperature I-climate-properties
increased O
by O
1.2 O
° O
C O
, O
and O
annual B-climate-properties
precipitation I-climate-properties
increased O
by O
140 O
mm O
. O

However O
, O
no O
significant O
trend O
of O
annual B-climate-properties
runoff I-climate-properties
during O
the O
last O
20 O
years O
could O
be O
found O
. O

Air B-climate-properties
temperature I-climate-properties
and O
precipitation B-climate-properties
changes I-climate-properties
were O
significant O
during O
winter O
and O
spring O
but O
very O
small O
in O
summer O
. O

Coniferous B-climate-nature
and O
mixed B-climate-nature
coniferous I-climate-nature
- I-climate-nature
broadleaf I-climate-nature
forests I-climate-nature
cover O
at O
present O
about O
72 O
% O
of O
the O
catchment B-climate-nature
area I-climate-nature
. O

Transpiration O
of O
the O
boreal B-climate-nature
forest I-climate-nature
ecosystem O
measured O
using O
a O
sap O
flow O
method O
during O
the O
dry O
summer O
1999 O
was O
limited O
by O
very O
dry O
soil B-climate-nature
water I-climate-nature
conditions O
, O
especially O
for O
spruce B-climate-organisms
trees I-climate-organisms
, O
and O
during O
the O
rainy O
summer O
2000 O
probably O
by O
lack O
of O
oxygen O
in O
the O
rooting O
zone O
. O

Transpiration B-climate-nature
was O
about O
10–20 O
% O
larger O
for O
broadleaf B-climate-organisms
trees I-climate-organisms
( O
birch B-climate-organisms
, O
aspen B-climate-organisms
) O
than O
for O
spruce B-climate-organisms
trees I-climate-organisms
. O

-DOCSTART- -X- O O 9fd1e4a13963002b632c9fd0cfe9659f

We O
construct O
a O
model O
accounting O
for O
pollution B-climate-hazards
damages B-climate-impacts
and O
the O
option O
to O
use O
a O
depletable O
and O
a O
renewable B-climate-mitigations
energy I-climate-mitigations
resource O
in O
production O
. O

We O
start O
by O
setting O
up O
an O
economy O
including O
for O
pollution B-climate-hazards
and O
the O
key O
distinguishing O
factors O
between O
depletable O
and O
renewable B-climate-mitigations
resources I-climate-mitigations
. O

It O
will O
be O
key O
not O
to O
solely O
rely O
on O
changes O
in O
the O
energy B-climate-mitigations
mix I-climate-mitigations
but O
to O
move O
to O
an O
economy O
that O
can O
substitute O
renewable B-climate-mitigations
energy I-climate-mitigations
resources O
for O
fossil B-climate-problem-origins
fuels I-climate-problem-origins
. O
- O

-DOCSTART- -X- O O b4634a24d927260491efbc2843573447

Abstract O
Drawing O
on O
ethnographic O
fieldwork O
in O
Trinidad O
, O
this O
paper O
examines O
how O
the O
framing O
of O
a O
particular O
apocalyptic O
future O
provided O
a O
moral O
commentary O
and O
model O
for O
wellbeing O
in O
contemporary O
everyday O
life O
. O

These O
more O
recent O
problems O
originating O
from O
beyond O
the O
village O
( O
such O
as O
climate O
change O
, O
criminality O
, O
inequality O
, O
pollution B-climate-hazards
, O
neglect O
by O
the O
State O
) O
could O
not O
be O
resolved O
through O
working O
with O
obeah O
spirits O
as O
might O
have O
been O
used O
previously O
for O
more O
local O
issues O
, O
or O
through O
the O
long O
- O
established O
Catholic O
and O
Anglican O
churches O
. O

-DOCSTART- -X- O O c2efdff889b2a56094a9edce2decde27

All O
analyzed O
risks O
to O
transport B-climate-assets
infrastructure I-climate-assets
are O
found O
to O
increase O
over O
the O
decades O
ahead O
with O
accelerating O
pace O
towards O
the O
end O
of O
this O
century O
. O

Mean O
Fennoscandian O
winter B-climate-properties
temperatures I-climate-properties
by O
the O
end O
of O
this O
century O
may O
match O
conditions O
of O
rather O
warm O
winter O
season O
experienced O
in O
the O
past O
and O
particularly O
warm O
future O
winter B-climate-properties
temperatures I-climate-properties
have O
not O
been O
observed O
so O
far O
. O

Occurrence B-climate-properties
frequencies I-climate-properties
of O
extreme B-climate-hazards
climate I-climate-hazards
phenomena I-climate-hazards
triggering O
landslides B-climate-hazards
and O
rutting B-climate-hazards
events O
in O
Central O
Europe O
are O
also O
projected O
to O
rise O
. O

-DOCSTART- -X- O O e5359cbcca508f52f30dd3d2775c2197

The O
fast O
depletion O
of O
fossil B-climate-problem-origins
fuels I-climate-problem-origins
, O
climate O
change O
, O
and O
global O
warming O
have O
become O
major O
worldwide O
problems O
and O
alternatives O
for O
conventional B-climate-problem-origins
transportation I-climate-problem-origins
have O
been O
actively O
researched O
in O
the O
last O
decade O
. O

Compared O
to O
available O
conventional B-climate-problem-origins
vehicles I-climate-problem-origins
, O
electric B-climate-mitigations
vehicles I-climate-mitigations
have O
a O
leading O
position O
due O
to O
their O
environmentally B-climate-mitigations
friendly I-climate-mitigations
transportation I-climate-mitigations
. O

RDeployment O
planning O
of O
charging O
stations O
is O
very O
important O
for O
driver O
expectations O
and O
social O
and O
economic O
impacts O
of O
electric B-climate-mitigations
vehicles I-climate-mitigations
. O

In O
this O
regard O
, O
optimizing O
the O
number O
of O
charging O
stations O
and O
their O
locations O
is O
very O
important O
for O
wide O
- O
scale O
use O
of O
electric B-climate-mitigations
vehicles I-climate-mitigations
. O

-DOCSTART- -X- O O 07ca9c1263845299130d406e271a5be7

The O
analysis O
has O
been O
conducted O
with O
a O
Multi B-climate-models
- I-climate-models
sector I-climate-models
Macroeconomic I-climate-models
Model I-climate-models
for I-climate-models
the I-climate-models
Evaluation I-climate-models
of I-climate-models
environmental I-climate-models
and I-climate-models
Energy I-climate-models
policy I-climate-models
( O
Three B-climate-models
- I-climate-models
ME I-climate-models
) O
. O

Three B-climate-models
- I-climate-models
ME I-climate-models
estimates O
the O
carbon B-climate-mitigations
tax I-climate-mitigations
required O
to O
meet O
emissions B-climate-mitigations
reduction I-climate-mitigations
targets I-climate-mitigations
within O
the O
Mexican O
“ O
Climate B-climate-mitigations
Change I-climate-mitigations
Law I-climate-mitigations
” O
, O
and O
assesses O
alternative O
policy O
scenarios O
, O
ach O
reflecting O
a O
different O
strategy O
for O
the O
recycling O
of O
tax O
revenues O
. O

With O
no O
compensation O
, O
the O
taxation B-climate-mitigations
policy I-climate-mitigations
if O
successful O
will O
succeed O
in O
reducing O
CO2 B-climate-greenhouse-gases
e O
missions O
by O
more O
than O
75 O
% O
by O
2050 O
with O
respect O
to O
Business O
as O
Usual O
( O
BAU O
) O
, O
but O
at O
high O
eco O
nomic O
costs O
. O

-DOCSTART- -X- O O 1ad4f6e90fe43cefdd1f6694a60b11ff

Surface B-climate-nature
melting I-climate-nature
over O
the O
Antarctic O
Peninsula O
( O
AP O
) O
may O
impact O
the O
stability O
of O
ice B-climate-nature
shelves I-climate-nature
and O
therefore O
the O
rate O
at O
which O
grounded B-climate-nature
ice I-climate-nature
is O
discharged O
into O
the O
ocean B-climate-nature
. O

Energy B-climate-properties
and I-climate-properties
mass I-climate-properties
balance I-climate-properties
models O
are O
needed O
to O
understand O
how O
climatic O
change O
and O
atmospheric B-climate-nature
circulation I-climate-nature
variability I-climate-nature
drive O
current O
and O
future O
melting B-climate-nature
. O

In O
this O
study O
, O
we O
evaluate O
the O
regional O
climate O
model O
MAR B-climate-models
over O
the O
AP O
at O
a O
10 O
km O
spatial O
resolution O
between O
1999 O
and O
2009 O
, O
a O
period O
when O
active O
microwave O
data O
from O
the O
QuikSCAT B-climate-observations
mission O
is O
available O
. O

A O
comparison O
of O
MAR B-climate-models
with O
satellite O
and O
automatic O
weather O
station O
( O
AWS O
) O
data O
reveals O
that O
satellite O
estimates O
show O
greater O
melt B-climate-properties
frequency I-climate-properties
, O
a O
larger O
melt B-climate-properties
extent I-climate-properties
, O
and O
a O
quicker O
expansion O
to O
peak B-climate-properties
melt I-climate-properties
extent I-climate-properties
than O
MAR B-climate-models
in O
the O
center O
and O
east O
of O
the O
Larsen O
C O
ice O
shelf O
. O

-DOCSTART- -X- O O 316dc9c4b4d159499ab7ee721bad02cf

is O
a O
disease B-climate-impacts
caused O
by O
the O
chikungunya B-climate-hazards
virus I-climate-hazards
( O
CHIKV B-climate-hazards
) O
, O
a O
mosquito B-climate-hazards
- O
borne O
alphavirus B-climate-hazards
that O
is O
emerging O
and O
re O
- O
emerging O
in O
different O
parts O
of O
the O
world O
. O

Urban B-climate-problem-origins
expansion I-climate-problem-origins
, O
globalization O
, O
increase O
in O
human O
travel B-climate-problem-origins
and O
tourism B-climate-problem-origins
are O
some O
of O
the O
factors O
increasing O
the O
risk O
of O
importation O
of O
the O
disease B-climate-impacts
in O
new O
areas O
. O

-DOCSTART- -X- O O f6c5dfdbc7bd62328c3854ccfcbfd497

Land B-climate-nature
surface I-climate-nature
models O
are O
essential O
parts O
of O
climate O
and O
weather O
models O
. O

-DOCSTART- -X- O O 9a62b7fd31e49b13c4648e036378a417

Abstract O
The O
runoff B-climate-nature
regime O
of O
glacierized B-climate-nature
headwater I-climate-nature
catchments I-climate-nature
in O
the O
Alps O
is O
essentially O
characterized O
by O
snow B-climate-nature
and O
ice B-climate-nature
melt I-climate-nature
. O

High O
Alpine O
drainage B-climate-nature
basins I-climate-nature
influence O
distant O
downstream O
catchments B-climate-nature
of O
the O
Rhine O
River O
basin O
. O

In O
particular O
, O
during O
the O
summer O
months O
, O
low O
- O
flow O
conditions O
are O
probable O
with O
strongly O
reduced O
snow B-climate-nature
and O
ice B-climate-nature
melt I-climate-nature
under O
climate O
change O
conditions O
. O

For O
the O
small O
Silvretta O
catchment O
( O
103 O
km2 O
) O
in O
the O
Swiss O
Alps O
, O
with O
a O
glacierization B-climate-nature
of O
7 O
% O
, O
the O
HBV B-climate-models
model O
and O
the O
glacio B-climate-nature
- I-climate-nature
hydrological I-climate-nature
model O
GERM B-climate-models
are O
applied O
for O
calculating O
future O
runoff B-climate-nature
based O
on O
different O
regional O
climate O
scenarios O
. O

Comparison O
of O
the O
models O
indicates O
that O
the O
HBV B-climate-models
model O
strongly O
overestimates O
the O
future O
contribution O
of O
glacier B-climate-nature
melt I-climate-nature
to O
runoff O
, O
as O
glaciers B-climate-nature
are O
considered O
as O
static O
components O
. O

Furthermore O
, O
we O
provide O
estimates O
of O
the O
current O
meltwater B-climate-nature
contribution O
of O
glaciers B-climate-nature
for O
several O
catchments B-climate-nature
downstream O
on O
the O
River O
Rhine O
during O
the O
month O
of O
August O
. O

-DOCSTART- -X- O O b7a2e4b95ce4e1e220a871c9ddf2c83f

prevalence O
study O
have O
been O
conducted O
in O
Sidi O
Kacem O
province O
in O
Morocco O
in O
2012 O
, O
1201 O
cattle B-climate-assets
were O
screened O
using O
single O
comparative O
intradermal O
tuberculin O
skin O
test O
, O
the O
apparent O
prevalence O
was O
20.4 O
% O
and O
57.7 O
% O
in O
the O
individual O
and O
herd O
level O
respectively O
. O

-DOCSTART- -X- O O 3f630ff17ed3c987a9b84da06c15f577

Energy B-climate-properties
efficiency I-climate-properties
of O
a O
building B-climate-assets
has O
become O
a O
major O
requirement O
since O
the O
building B-climate-assets
sector O
produces O
40%–50 O
% O
of O
the O
global O
greenhouse O
gas O
emissions O
. O

This O
can O
be O
achieved O
by O
improving O
building O
’s O
performance O
through O
energy B-climate-mitigations
savings I-climate-mitigations
, O
by O
adopting O
energy B-climate-mitigations
- I-climate-mitigations
efficient I-climate-mitigations
technologies I-climate-mitigations
and O
by O
reducing O
CO2 B-climate-greenhouse-gases
emissions O
. O

Earth B-climate-mitigations
pipe I-climate-mitigations
cooling I-climate-mitigations
system I-climate-mitigations
is O
one O
of O
them O
, O
which O
works O
with O
a O
long O
buried O
pipe O
with O
one O
end O
for O
intaking O
air O
and O
the O
other O
end O
for O
providing O
air O
cooled O
by O
soil B-climate-nature
to O
the O
building B-climate-assets
. O

An O
integrated O
numerical O
model O
for O
the O
horizontal O
earth B-climate-mitigations
pipe I-climate-mitigations
cooling I-climate-mitigations
system I-climate-mitigations
and O
the O
room O
( O
or O
building B-climate-assets
) O
was O
developed O
using O
ANSYS B-climate-models
Fluent I-climate-models
to O
measure O
the O
thermal B-climate-properties
performance I-climate-properties
of O
the O
system O
. O

The O
impact O
of O
air B-climate-properties
temperature I-climate-properties
, O
soil B-climate-properties
temperature I-climate-properties
, O
air B-climate-properties
velocity I-climate-properties
and O
relative B-climate-properties
humidity I-climate-properties
on O
room O
cooling O
performance O
has O
also O
been O
assessed O
. O

-DOCSTART- -X- O O 359336e84d97cfb3afa8bda3c01398c7

Warming O
winters O
due O
to O
climate O
change O
may O
critically O
affect O
temperate B-climate-organisms
tree I-climate-organisms
species I-climate-organisms
. O

Insufficiently O
cold O
winters O
are O
thought O
to O
result O
in O
fewer O
viable O
flower B-climate-nature
buds I-climate-nature
and O
the O
subsequent O
development O
of O
fewer O
fruits B-climate-assets
or O
nuts B-climate-assets
, O
decreasing O
the O
yield O
of O
an O
orchard B-climate-assets
or O
fecundity O
of O
a O
species B-climate-organisms
. O

County O
- O
wide O
yield B-climate-assets
records O
for O
almond B-climate-assets
( O
Prunus B-climate-assets
dulcis I-climate-assets
) O
, O
pistachio B-climate-assets
( O
Pistacia B-climate-assets
vera I-climate-assets
) O
, O
and O
walnut B-climate-assets
( O
Juglans B-climate-assets
regia I-climate-assets
) O
in O
the O
Central O
Valley O
of O
California O
were O
compared O
with O
50 O
years O
of O
weather B-climate-nature
records O
. O

-DOCSTART- -X- O O 174556578ac968fa13ee4c7453e9ff77

The O
enhancement O
of O
bioethanol B-climate-mitigations
production O
means O
from O
different O
types O
of O
biomass O
presents O
significant O
problems O
and O
engineering O
challenges O
. O

Due O
to O
climate O
and O
a O
wellestablished O
sugar B-climate-mitigations
- I-climate-mitigations
cane I-climate-mitigations
ethanol I-climate-mitigations
production O
, O
Brazil O
is O
in O
a O
privileged O
position O
in O
the O
global O
ethanol O
production O
scenario O
. O

Improvements O
on O
this O
process O
can O
have O
a O
significant O
effect O
in O
several O
stages O
of O
production O
, O
once O
the O
production O
process O
is O
used O
both O
for O
first O
and O
secondgeneration B-climate-mitigations
ethanol I-climate-mitigations
. O

Most O
studies O
have O
investigated O
how O
to O
improve O
parameters O
such O
as O
shear B-climate-properties
stress I-climate-properties
, O
velocity B-climate-properties
profiles I-climate-properties
and O
residence B-climate-properties
time I-climate-properties
, O
and O
of O
the O
influence O
of O
the O
bioreactor O
geometry O
on O
the O
parameters O
. O

-DOCSTART- -X- O O 3067f87dde415eb8ef7814f0a0695d5f

In O
order O
to O
limit O
future O
coastal B-climate-hazards
flood I-climate-hazards
risk O
, O
adaptation O
is O
necessary O
. O

This O
study O
presents O
an O
integrated O
model O
to O
simulate O
storm B-climate-hazards
surge I-climate-hazards
inundation I-climate-hazards
risk O
in O
Osaka O
Bay O
under O
climate O
change O
and O
provide O
a O
cost O
– O
benefit O
analysis O
of O
structure O
adaptation O
strategies O
to O
reduce O
risk O
. O

Without O
adaptation O
measures O
, O
the O
expected O
annual O
damage B-climate-impacts
cost I-climate-impacts
increases O
from O
9.85 O
billion O
JPY O
to O
69.17 O
billion O
JPY O
in O
Osaka O
Bay O
under O
the O
projected O
RCP8.5 B-climate-datasets
scenario O
to O
2100 O
. O

The O
results O
indicate O
that O
raising O
the O
height B-climate-properties
of O
existing O
dikes B-climate-mitigations
can O
reduce O
inundation B-climate-hazards
risk O
effectively O
. O

Using O
cost O
– O
benefit O
analysis O
, O
we O
find O
that O
upgrading O
by O
1 O
m O
the O
height B-climate-properties
of O
existing O
dikes B-climate-mitigations
is O
the O
most O
cost O
- O
effective O
strategy O
for O
Osaka O
Bay O
. O

-DOCSTART- -X- O O 3fc2c704c3708a2405ea90fd81838d1f

Landscape O
- O
level O
processes O
such O
as O
fire B-climate-hazards
regimes O
, O
increasing O
disease B-climate-impacts
prevalence O
and O
a O
drying O
climate O
are O
emerging O
threats O
affecting O
plant B-climate-organisms
groups O
such O
as O
the O
Proteaceae B-climate-organisms
. O

We O
found O
the O
persistence O
of O
B. B-climate-organisms
verticillata I-climate-organisms
on O
granite B-climate-nature
inselbergs I-climate-nature
is O
strongly O
influenced O
by O
fire B-climate-hazards
frequency I-climate-hazards
and O
extent O
, O
as O
well O
as O
the O
prevalence O
of O
canker B-climate-impacts
disease I-climate-impacts
. O

-DOCSTART- -X- O O 42c35f310d2fef01828b7fe3c77e59bf

This O
study O
isolates O
the O
impact O
of O
an O
open O
Southern O
Ocean O
gateway O
upon O
the O
interhemispheric O
asymmetry O
in O
transient O
global O
warming O
by O
forcing O
a O
fully O
coupled O
climate O
model O
with O
an O
increasing O
CO2 B-climate-greenhouse-gases
scenario O
with O
and O
without O
a O
land O
bridge O
across O
Drake O
Passage O
( O
DP O
) O
. O

These O
results O
illustrate O
that O
part O
of O
the O
interhemispheric O
asymmetry O
in O
surface O
warming O
is O
due O
to O
the O
Antarctic B-climate-nature
Circumpolar I-climate-nature
Current I-climate-nature
( O
ACC B-climate-nature
) O
thermally O
isolating O
Antarctica O
. O

-DOCSTART- -X- O O b9f344daf90647aa62b988e54f23dff7

To O
date O
, O
climate O
change O
vulnerability O
assessments O
have O
largely O
been O
based O
on O
projected O
changes O
in O
range B-climate-properties
size I-climate-properties
derived O
from O
the O
output O
of O
species O
distribution O
models O
( O
SDMs O
) O
. O

-DOCSTART- -X- O O 34a98ef0ba09b6a7e3d78457dc3670b7

Subtropical B-climate-nature
marine B-climate-nature
stratocumulus I-climate-nature
clouds I-climate-nature
have O
a O
pronounced O
impact O
on O
the O
global O
shortwave B-climate-nature
radiation I-climate-nature
budget I-climate-nature
and O
may O
be O
an O
important O
feedback O
process O
in O
the O
topic O
of O
global O
climate O
change O
. O

Large B-climate-models
eddy I-climate-models
simulation I-climate-models
( O
LES B-climate-models
) O
models O
have O
supplied O
insight O
into O
these O
cloudtopped B-climate-nature
boundary I-climate-nature
layers I-climate-nature
( O
CTBLs B-climate-nature
) O
and O
show O
that O
cloud B-climate-nature
macroscopic O
properties O
such O
as O
cloud B-climate-properties
base I-climate-properties
, O
height B-climate-properties
, O
and O
fraction O
result O
from O
a O
delicate O
balance O
between O
surface B-climate-nature
fluxes I-climate-nature
, O
cloud B-climate-nature
radiative I-climate-nature
fluxes I-climate-nature
, O
large O
scale O
subsidence B-climate-nature
, O
and O
when O
present O
, O
precipitation B-climate-nature
in O
the O
form O
of O
drizzle B-climate-nature
. O

In O
this O
study O
the O
US B-climate-models
Navy I-climate-models
Coupled I-climate-models
Ocean I-climate-models
/ I-climate-models
Atmosphere I-climate-models
Mesoscale I-climate-models
Prediction I-climate-models
System I-climate-models
( O
COAMPS I-climate-models
†† O
; O
Hodur O
1997 O
) O
has O
been O
applied O
to O
a O
case O
of O
coastal O
California O
summer O
season O
stratocumulus B-climate-nature
. O

-DOCSTART- -X- O O >>> bpf complete annotation here

-DOCSTART- -X- O O e319e8745b280c4e101220b8f1788537

The O
workshop O
was O
convened O
by O
a O
LIMPACS B-climate-organizations
working I-climate-organizations
group I-climate-organizations
under O
the O
auspices O
of O
the O
PAGES B-climate-organizations
( I-climate-organizations
Past I-climate-organizations
Global I-climate-organizations
Change I-climate-organizations
) I-climate-organizations
program I-climate-organizations
, O
which O
is O
an O
initiative O
under O
the O
International B-climate-organizations
Geosphere I-climate-organizations
Biosphere I-climate-organizations
Program I-climate-organizations
( O
IGBP B-climate-organizations
) O
. O

LIMPACS I-climate-organizations
focuses O
on O
lake B-climate-nature
ecosystems O
, O
and O
this O
specific O
working O
group O
within O
LIMPACS B-climate-organizations
considers O
how O
sediment B-climate-nature
records O
, O
monitoring O
, O
and O
modeling O
can O
be O
integrated O
to O
better O
understand O
lake B-climate-nature
ecosystems O
and O
climate O
variability O
in O
semi O
- O
arid O
regions O
, O
particularly O
the O
record O
and O
dynamics O
of O
saline B-climate-nature
lakes I-climate-nature
. O

-DOCSTART- -X- O O bc8d080577bd3ccf2280cfa00eb3c2b7

However O
, O
the O
sustainability O
of O
this O
practice O
which O
implies O
systematic O
removal O
of O
aerial B-climate-properties
biomass I-climate-properties
of O
cereal B-climate-assets
crops I-climate-assets
is O
a O
controversial O
issue O
, O
particularly O
in O
soils O
having O
a O
low O
soil B-climate-nature
organic I-climate-nature
carbon I-climate-nature
( O
SOC B-climate-nature
) O
content O
. O

This O
study O
aims O
at O
evaluating O
a O
simple O
model O
( O
AMG B-climate-models
) O
to O
predict O
the O
consequences O
of O
straw B-climate-assets
export O
on O
SOC B-climate-nature
evolution O
in O
various O
cropping O
and O
pedoclimatic O
conditions O
. O

The O
dependence O
of O
model O
parameters O
( O
humification B-climate-properties
and I-climate-properties
mineralization I-climate-properties
rates I-climate-properties
) O
on O
pedoclimatic O
conditions O
( O
soil B-climate-properties
clay I-climate-properties
content I-climate-properties
and O
temperature B-climate-properties
) O
was O
analyzed O
and O
compared O
to O
those O
proposed O
in O
other O
models O
( O
DAISY B-climate-models
, O
CENTURY B-climate-models
, O
ROTHC B-climate-models
, O
CN B-climate-models
- I-climate-models
SIM I-climate-models
) O
since O
they O
vary O
widely O
between O
models O
. O

The O
AMG B-climate-models
model O
was O
used O
to O
simulate O
the O
impact O
of O
a O
straw B-climate-assets
export O
scenario O
in O
nine O
experiments O
considering O
a O
systematic O
straw B-climate-assets

-DOCSTART- -X- O O f3ca78ee9e3cf36ab2dc676891272d17

This O
study O
develops O
an O
innovative O
approach O
that O
combines O
three O
physical O
climate O
simulations O
( O
Geophysical B-climate-organizations
Fluid I-climate-organizations
Dynamics I-climate-organizations
Laboratory I-climate-organizations
model O
, O
Goddard B-climate-organizations
Institute I-climate-organizations
of I-climate-organizations
Space I-climate-organizations
Studies I-climate-organizations
model O
, O
and O
the O
United B-climate-organizations
Kingdom I-climate-organizations
Meteorological I-climate-organizations
Office I-climate-organizations
climate O
model O
) O
with O
a O
general O
equilibrium O
model O
of O
global O
trade O
in O
order O
to O
study O
the O
real O
economic O
impacts O
of O
climate O
change O
. O

-DOCSTART- -X- O O 19a23fe5aa6364986ea4ca3826a60dbf

CCI B-climate-models
- I-climate-models
HYDR I-climate-models
provides O
three O
scenarios O
, O
tailored O
for O
Belgium O
every O
decade O
until O
2100 O
. O

In O
contrast O
, O
KNMI B-climate-models
- I-climate-models
ADC I-climate-models
tool O
provides O
191 O
scenarios O
, O
at O
a O
regional O
level O
and O
for O
two O
horizons O
( O
near O
and O
far O
future O
) O
. O

-DOCSTART- -X- O O 5ec835e0d28da27519cea99c99dcf8bf

This O
study O
investigates O
changes O
in O
hourly O
extreme O
driving B-climate-properties
rain I-climate-properties
wind I-climate-properties
pressure I-climate-properties
( O
DRWP B-climate-properties
) O
—a O
climatic O
variable O
used O
in O
building B-climate-assets
design O
in O
Canada O
— O
for O
future O
periods O
of O
specified O
global B-climate-properties
mean I-climate-properties
temperature I-climate-properties
change O
using O
an O
ensemble O
of O
a O
Canadian B-climate-models
regional I-climate-models
climate I-climate-models
model I-climate-models
( O
CanRCM4 B-climate-models
) O
driven O
by O
the O
Canadian B-climate-models
Earth I-climate-models
system I-climate-models
model I-climate-models
( O
CanESM2 B-climate-models
) O
under O
the O
Representative B-climate-datasets
Concentration I-climate-datasets
Pathway I-climate-datasets
8.5 I-climate-datasets
scenario O
. O

Evaluation O
of O
the O
model O
shows O
that O
the O
CanRCM4 B-climate-models
ensemble O
reproduces O
hourly O
extreme O
wind B-climate-nature
speeds I-climate-nature
and O
rainfall B-climate-nature
( O
> O
1.8 O
mm O
/ O
h O
) O
occurrence B-climate-properties
frequency I-climate-properties
and O
the O
associated O
design O
( O
5 O
- O
year O
return O
level O
) O
DRWP B-climate-properties
across O
Canada O
well O
when O
compared O
with O
130 O
meteorological O
stations O
. O

Future O
risk O
ratios O
of O
the O
design O
DRWP O
are O
highly O
dependent O
on O
those O
of O
the O
rainfall B-climate-nature
occurrence O
, O
which O
shows O
large O
increases O
over O
the O
three O
regions O
, O
while O
they O
are O
partly O
affected O
by O
the O
increases O
in O
future O
extreme O
wind B-climate-nature
speeds I-climate-nature
over O
western O
and O
northeastern O
Canada O
. O

-DOCSTART- -X- O O 61b443a701f91758f5f38ada4f5128c7

Ahead O
of O
COP21 B-climate-organizations
in O
Paris O
, O
countries O
have O
tabled O
their O
emissions O
reductions O
pledges O
in O
the O
form O
of O
Intended O
Nationally O
Determined O
Contributions O
( O
INDCs O
) O
. O

-DOCSTART- -X- O O ddb24f23a7a9fdb26e85d9479ca3291f

SALTRACE B-climate-observations
, O
the O
DLR B-climate-observations
Falcon I-climate-observations
research I-climate-observations
aircraft I-climate-observations
was O
based O
at O
Sal O
, O
Cape O
Verde O
, O
between O
11 O
and O
17 O
June O
2013 O
, O
and O
at O
Barbados O
between O
18 O
June O
and O
11 O
July O
2013 O
. O

Ground O
- O
based O
lidar I-climate-observations
and O
in O
- O
situ O
instruments O
were O
deployed O
in O
Cape O
Verde O
, O
Barbados O
and O
Puerto O
Rico O
. O

-DOCSTART- -X- O O c50ab4b826a30d23fbb085809ae7aa3f

CRNS B-climate-observations
moderated O
neutron O
counts O
were O
compared O
to O
manual O
snow B-climate-nature
survey O
SWE O
values O
obtained O
during O
both O
winter O
seasons O
. O

-DOCSTART- -X- O O 6c3ea1bc86380caf15491455c3879167

Among O
US O
studies O
where O
prevailing O
methods O
such O
as O
chemical O
mass O
balance O
( O
CMB O
) O
and O
positive O
matrix O
factorization O
( O
PMF O
) O
models O
have O
allowed O
for O
estimation O
of O
more O
refined O
source O
contributions O
, O
there O
are O
fewer O
findings O
showing O
the O
significance O
of O
biomass O
burning O
and O
variable O
findings O
on O
the O
relative O
proportion O
of O
BC B-climate-greenhouse-gases
attributed O
to O
diesel B-climate-problem-origins
versus O
gasoline B-climate-problem-origins
emissions O
. O

-DOCSTART- -X- O O 4fb290d09d64924e35766b15cd12a0b7

We O
use O
the O
Weather B-climate-models
Research I-climate-models
and I-climate-models
Forecast I-climate-models
( O
WRF B-climate-models
) O
model O
driven O
by O
the O
operational O
analysis O
of O
the O
European B-climate-organizations
Centre I-climate-organizations
for I-climate-organizations
Medium I-climate-organizations
- I-climate-organizations
Range I-climate-organizations
Weather I-climate-organizations
Forecasting I-climate-organizations
( O
ECMWF B-climate-organizations
) O
to O
investigate O
the O
impact O
of O
these O
events O
on O
the O
positions O
and O
strength O
of O
the O
heat O
low O
in O
convection O
- O
permitting O
simulations O
. O

-DOCSTART- -X- O O b237f39ef9d1589c5442af30f8c77b57

The O
EPIC B-climate-models
simulation O
model O
was O
used O
to O
assess O
the O
impact O
of O
climate O
change O
( O
CC O
) O
on O
intensive O
and O
extensive O
Mediterranean O
forage B-climate-assets
systems O
to O
study O
the O
effects O
of O
CC O
and O
adaptation O
strategies O
. O

-DOCSTART- -X- O O ddc20af19c2921f5bd0d702004f6262f

Using O
the O
temperature B-climate-properties
suitability O
of O
premium O
winegrape B-climate-assets
cultivation O
as O
a O
climate O
impacts O
indicator O
, O
we O
quantify O
the O
inter- O
and O
intra O
- O
ensemble O
spread O
in O
three O
climate O
model O
ensembles O
: O
a O
physically O
uniform O
multi O
- O
member O
ensemble O
consisting O
of O
the O
RegCM3 B-climate-models
high O
- O
resolution O
climate O
model O
nested O
within O
the O
NCAR B-climate-models
CCSM3 I-climate-models
global O
climate O
model O
; O
the O
multi O
- O
model O
NARCCAP B-climate-models
ensemble O
consisting O
of O
single O
realizations O
of O
multiple O
high O
- O
resolution O
climate O
models O
nested O
within O
multiple O
global O
climate O
models O
; O
and O
the O
multi O
- O
model O
CMIP3 B-climate-models
ensemble O
consisting O
of O
realizations O
of O
multiple O
global O
climate O
models O
. O

In O
addition O
, O
the O
intra O
- O
ensemble O
spread O
is O
similar O
in O
the O
NARCCAP B-climate-models
nested O
climate O
model O
ensemble O
and O
the O
CMIP3 B-climate-models
global O
climate O
model O
ensemble O
, O
suggesting O
that O
the O
uncertainty O
arising O
from O
the O
model O
formulation O
of O
fine O
- O
scale O
climate O
processes O
is O
not O
smaller O
than O
the O
uncertainty O
arising O
from O
the O
formulation O
of O
large O
- O
scale O
climate O
processes O
. O

-DOCSTART- -X- O O 1231d77f26db4695bf428973c1f21bf7

Based O
on O
the O
time O
series O
of O
the O
normalized B-climate-observations
difference I-climate-observations
vegetation I-climate-observations
index I-climate-observations
( O
NDVI B-climate-observations
) O
derived O
from O
satellite O
observations O
, O
the O
decadal O
change O
in O
vegetation B-climate-nature
can O
be O
examined O
. O

Additionally O
, O
we O
suggest O
that O
the O
relative O
sparseness O
of O
the O
boreal B-climate-nature
forest I-climate-nature
may O
be O
suitable O
for O
AGB B-climate-properties
estimation O
by O
microwave O
radar O
remote O
sensing O
. O

-DOCSTART- -X- O O 1fdec2dc60c5d9945c2e344e52df845f

Methods O
Crop B-climate-assets
and O
livestock B-climate-assets
production O
from O
representative O
systems O
were O
simulated O
over O
40 O
years O
at O
six O
locations O
spanning O
Australia O
's O
crop B-climate-assets
- O
livestock B-climate-assets
zone O
using O
coupled O
biophysical O
production O
simulation O
models O
, O
APSIM B-climate-models
for O
cropping B-climate-assets
enterprises O
and O
GRAZPLAN B-climate-models
for O
livestock B-climate-assets
enterprises O
. O

-DOCSTART- -X- O O a123c43e4baeff22ce62bcdc874821ff

In O
this O
study O
, O
the O
outputs O
of O
mid O
- O
Holocene O
, O
pre O
- O
industrial O
and O
CO2 B-climate-greenhouse-gases
- O
induced O
warming O
experiments O
using O
a O
coupled O
climate O
model O
( O
MPI B-climate-models
- I-climate-models
ESM I-climate-models
- I-climate-models
P I-climate-models
) O
were O
employed O
to O
examine O
the O
changes O
in O
extreme B-climate-hazards
precipitation I-climate-hazards
over O
East O
Asia O
in O
the O
mid O
- O
Holocene O
and O
under O
future O
warming O
scenario O
, O
respectively O
. O

Moisture O
budget O
analysis O
shows O
that O
these O
distinct O
responses O
come O
from O
different O
changes O
in O
moisture B-climate-properties
and O
circulation O
during O
the O
two O
periods O
. O

During O
the O
mid O
- O
Holocene O
, O
higher O
insolation B-climate-properties
over O
the O
Northern O
Hemisphere O
leads O
to O
a O
larger O
ocean B-climate-nature
– I-climate-nature
land I-climate-nature
thermal I-climate-nature
contrast I-climate-nature
and O
a O
stronger O
East B-climate-nature
Asian I-climate-nature
summer I-climate-nature
monsoon I-climate-nature
( O
EASM B-climate-nature
) O
. O

-DOCSTART- -X- O O fc714f51f3e52e379b82a79b3e60ce1f

Coarse O
grid O
atmospheric B-climate-nature
parameters O
provided O
by O
GCM O
models O
for O
A2 B-climate-datasets
and I-climate-datasets
B2 I-climate-datasets
scenarios O
of O
IPCC B-climate-organizations
[ O
1 O
] O
are O
downscaled O
to O
catchment B-climate-nature
scale O
by O
the O
application O
of O
Statistical O
Downscaling O
Model O
( O
SDSM O
) O
. O

Flood B-climate-hazards
discharge I-climate-hazards
and O
inundation B-climate-hazards
along O
the O
Kelani O
River O
reach O
below O
Hanwella O
was O
analyzed O
by O
the O
application O
of O
two O
- O
dimensional O
flood B-climate-hazards
simulation O
model O
( O
FLO-2D B-climate-models
) O
. O

Inflow O
to O
the O
model O
at O
Hanwella O
, O
is O
estimated O
by O
the O
HEC- B-climate-models
HMS I-climate-models
model O
under O
future O
extreme B-climate-hazards
rainfall I-climate-hazards
events O
. O

-DOCSTART- -X- O O ef63f1a6bf67208cb5893281d880be27

However O
, O
RCMs O
in O
North B-climate-models
American I-climate-models
Coordinated I-climate-models
Regional I-climate-models
Climate I-climate-models
Downscaling I-climate-models
Experiment I-climate-models
( O
NA B-climate-models
- I-climate-models
CORDEX I-climate-models
) O
and O
convection O
- O
permitting O
simulations O
of O
the O
climate O
of O
North O
America O
exhibit O
a O
significant O
decrease O
in O
surface B-climate-nature
solar I-climate-nature
radiation I-climate-nature
over O
large O
areas O
of O
the O
US O
. O

-DOCSTART- -X- O O df3c85139ddd94edf102b40f6e848777

We O
present O
a O
method O
for O
assessing O
windstorm B-climate-nature
damages B-climate-impacts
in O
forest B-climate-nature
landscapes O
based O
on O
a O
two O
- O
stage O
sampling O
strategy O
using O
single O
- O
date O
, O
post O
- O
event O
airborne B-climate-observations
laser I-climate-observations
scanning B-climate-observations
( O
ALS B-climate-observations
) O
data O
. O

The O
total B-climate-properties
volume I-climate-properties
of O
fallen B-climate-organisms
trees I-climate-organisms
is O
then O
estimated O
using O
a O
two O
- O
stage O
model O
- O
assisted O
approach O
, O
where O
variables O
from O
ALS B-climate-observations
are O
used O
as O
auxiliary O
information O
in O
the O
difference O
estimator O
. O

-DOCSTART- -X- O O c8c07a53fd7bf7c6d5cb78c8579c1a27

The O
Agri B-climate-properties
- I-climate-properties
environment I-climate-properties
Footprint I-climate-properties
Index I-climate-properties
( O
AFI B-climate-properties
) O
was O
developed O
as O
a O
generic O
methodology O
to O
assess O
farm B-climate-assets
- O
scale O
changes O
in O
the O
environmental O
impacts O
of O
agriculture B-climate-assets
and O
to O
assist O
the O
assessment O
of O
European O
agri O
- O
environment O
schemes O
. O

-DOCSTART- -X- O O 8babca50c8edebc7574abb5da73facf7

Within O
the O
project B-climate-observations
KLIFF I-climate-observations
, O
experiments O
were O
combined O
with O
species B-climate-organisms
distribution O
modelling O
for O
this O
task O
in O
the O
region O
of O
Lower O
Saxony O
, O
Germany O
. O

-DOCSTART- -X- O O 5ed3024380b9234ae2f0071650683fdf

For O
that O
purpose O
, O
time O
series O
of O
windpower B-climate-mitigations
production O
from O
wind B-climate-properties
speeds I-climate-properties
derived O
from O
measurements O
and O
two O
global O
climate O
reanalysis O
models O
( O
NCAR B-climate-models
and O
ECMWF B-climate-organizations
) O
are O
generated O
and O
validated O
. O

Our O
validation O
procedure O
shows O
that O
ECMWF B-climate-organizations
data O
may O
be O
the O
best O
source O
of O
long O
- O
term O
wind B-climate-nature
time O
series O
as O
it O
better O
reproduces O
ground O
measurements O
than O
NCAR B-climate-models
. O

-DOCSTART- -X- O O 3b8894241cee79f0b91b412991df19ef

In O
this O
study O
, O
we O
assess O
potential O
changes O
in O
fire B-climate-hazards
weather O
conditions O
for O
the O
contiguous O
United O
States O
using O
the O
Haines B-climate-properties
Index I-climate-properties
( O
HI B-climate-properties
) O
, O
a O
fire B-climate-hazards
weather O
index O
that O
has O
been O
employed O
operationally O
to O
detect O
atmospheric O
conditions O
favorable O
for O
large O
and O
erratic O
fire B-climate-hazards
behavior O
. O

We O
use O
simulations O
produced O
by O
the O
North B-climate-models
American I-climate-models
Regional I-climate-models
Climate I-climate-models
Change I-climate-models
Assessment I-climate-models
Program I-climate-models
( O
NARCCAP B-climate-models
) O
from O
multiple O
regional O
climate O
models O
( O
RCMs O
) O
driven O
by O
multiple O
general O
circulation O
models O
( O
GCMs O
) O
to O
examine O
changes O
by O
midcentury O
in O
the O
seasonal O
percentage O
of O
days O
and O
the O
consecutive O
number O
of O
days O
with O
high O
( O
values O
≥ O
5 O
) O
HI B-climate-properties
across O
the O
United O
States O
. O

-DOCSTART- -X- O O 8c0b1552984eb997eb07eb0fade51a57

The O
simulated O
climate O
scenarios O
used O
data O
from O
PRECIS B-climate-models
RCM I-climate-models
with O
A2 B-climate-datasets
and I-climate-datasets
B2 I-climate-datasets
scenarios O
. O

The O
future O
land O
use O
was O
created O
using O
a O
CA O
Markov O
model O
. O

Future O
runoff O
was O
generated O
using O
a O
SWAT B-climate-models
model O
. O

-DOCSTART- -X- O O 65197070b27f8ccae00e43d02b9555d7

physically O
based O
, O
distributed O
model O
MIKE B-climate-models
SHE I-climate-models
was O
used O
to O
simulate O
hydrology O
for O
the O
Lambourn O
Observatory O

-DOCSTART- -X- O O fa744d05565d6fa6dab123045cd3d977

Debris B-climate-nature
influences O
the O
surface O
energy O
balance O
and O
therefore O
glacier B-climate-nature
melt I-climate-nature
by O
influencing O
the O
thermal O
properties O
( O
e.g. O
albedo B-climate-properties
, O
thermal B-climate-properties
conductivity I-climate-properties
, O
roughness B-climate-properties
) O
of O
the O
glacier B-climate-nature
surface O
. O

We O
simulated O
a O
debris B-climate-nature
- O
covered O
glacier B-climate-nature
( O
Lirung O
Glacier O
, O
Nepal O
) O
at O
a O
high O
- O
resolution O
of O
1 O
m O
with O
the O
MicroHH B-climate-models
model O
with O
boundary O
conditions O
retrieved O
from O
an O
automatic O
weather O
station O
( O
temperature B-climate-properties
, O
wind B-climate-nature
and O
specific B-climate-properties
humidity I-climate-properties
) O
and O
UAV O
flights O
( O
digital O
elevation O
map O
and O
surface B-climate-properties
temperature I-climate-properties
) O
, O
and O
the O
model O
is O
validated O
with O
eddy B-climate-nature
covariance O
data O
. O

-DOCSTART- -X- O O 526bb950961c93bac7314c842070ce0f

Here O
we O
reconstruct O
the O
dynamic O
growth O
of O
thermokarst B-climate-nature
lakes I-climate-nature
and O
basins B-climate-nature
and O
related O
changes O
of O
depositional O
conditions O
preserved O
in O
sediment B-climate-nature
sequences I-climate-nature
using O
a O
combination O
of O
biogeochemical B-climate-nature
data O
and O
robust B-climate-models
grain I-climate-models
- I-climate-models
size I-climate-models
endmember I-climate-models
analysis I-climate-models
( O
rEMMA B-climate-models
) O
. O

-DOCSTART- -X- O O a4ccab57a7915da26a506447dd57c79f

In O
this O
study O
, O
the O
Weather B-climate-models
Research I-climate-models
and I-climate-models
Forecasting I-climate-models
with I-climate-models
Chemistry I-climate-models
( O
WRF B-climate-models
- I-climate-models
Chem I-climate-models
) O
community O
model O
, O
a O
state O
- O
of O
- O
the O
- O
art O
coupled O
meteorology O
- O
chemistry O
modelling O
system O
, O
along O
with O
experimental O
data O
collected O
during O
the O
Narrowing B-climate-datasets
the I-climate-datasets
Uncertainties I-climate-datasets
on I-climate-datasets
Aerosol I-climate-datasets
and I-climate-datasets
Climate I-climate-datasets
Change I-climate-datasets
in O
São O
Paulo O
State O
( O
NUANCE B-climate-datasets
- I-climate-datasets
SPS I-climate-datasets
, O
FAPESP B-climate-organizations
thematic O
project O
) O
campaigns O
performed O
in O
2012 O
and O
2014 O
, O
were O
used O
in O
order O
to O
examine O
the O
main O
properties O
of O
atmospheric B-climate-nature
aerosol B-climate-nature
particles O
over O
the O
Metropolitan O
Area O
of O
São O
Paulo O
( O
MASP O
) O
, O
in O
southeastern O
Brazil O
, O
where O
changes O
in O
fuel O
blend O
and O
consumption O
in O
recent O
years O
have O
affected O
the O
evolution O
of O
pollutant B-climate-properties
concentrations I-climate-properties
. O

-DOCSTART- -X- O O 2600aef1bffa023c8c9d9b4ff6106d1f

In O
this O
paper O
, O
we O
present O
the O
results O
of O
a O
study O
of O
the O
Weather B-climate-models
Research I-climate-models
and I-climate-models
Forecasting I-climate-models
( O
WRF B-climate-models
) O
model O
undertaken O
in O
order O
to O
assess O
if O
GPU O
and O
multicore O
acceleration O
of O
this O
type O
of O
numerical O
weather O
prediction O
( O
NWP O
) O
code O
is O
both O
feasible O
and O
worthwhile O
. O

-DOCSTART- -X- O O 603dd24b337ff5b64aeb6056e7e3b4af

Based O
on O
meteorological O
and O
satellite O
data O
, O
spatiotemporal O
dynamics O
of O
ET B-climate-nature
were O
detected O
using O
the O
Surface B-climate-models
Energy I-climate-models
Balance I-climate-models
System I-climate-models
( O
SEBS B-climate-models
) O
model O
, O
and O
effects O
of O
climate O
variability O
and O
landscape B-climate-problem-origins
pattern I-climate-problem-origins
change I-climate-problem-origins
on O
ET B-climate-nature
dynamics O
in O
an O
arid O
to O
semiarid O
landscape O
mosaic O
during O
the O
growing O
season O
( O
April‐October O
) O
from O
2001 O
to O
2015 O
in O
Xilingol O
League O
, O
China O
were O
evaluated O
. O

To O
promote O
effective O
water O
resource O
utilization O
, O
landscape O
managers O
should O
continue O
to O
moderately O
implement O
vegetation B-climate-mitigations
restoration I-climate-mitigations
projects I-climate-mitigations
such O
as O
the O
Grain B-climate-mitigations
for I-climate-mitigations
Green I-climate-mitigations
Project I-climate-mitigations
, O
orient O
with O
conversion O
of O
low‐quality O
cropland B-climate-assets
into O
grassland B-climate-nature
, O
and O
conserve O
large O
areas O
of O
grassland B-climate-nature
. O

-DOCSTART- -X- O O ba76b67e6108fa16e907d6ae80295ef7

These O
lakes B-climate-nature
generally O
grow O
, O
coalesce O
into O
larger O
lakes B-climate-nature
that O
may O
produce O
increased O
downstream O
hazards O
and O
risks O
due O
to O
glacial B-climate-hazards
lake I-climate-hazards
outburst I-climate-hazards
floods I-climate-hazards
( O
GLOFs B-climate-hazards
) O
. O

This O
study O
assesses O
such O
hazards O
of O
Lower O
Barun O
Lake O
located O
near O
Mount O
Everest O
, O
Nepal O
. O

-DOCSTART- -X- O O b538694eca96c876e203f5a82a4192d7

Gasoline O
transport O
by O
Kuwaiti B-climate-organizations
National I-climate-organizations
Petroleum I-climate-organizations
Company I-climate-organizations
( O
KNPC B-climate-organizations
) O
was O
selected O
as O
a O
case O
study O
. O

Consequently O
, O
an O
incident O
simulation O
scenario O
was O
developed O
by O
ALOHA B-climate-models
toolset O
( O
a O
dispersion O
model O
software O
) O
. O

-DOCSTART- -X- O O 8c5aca190540cccc33707635d2db891f

Cyclones B-climate-hazards
and O
associated O
storm B-climate-hazards
surges I-climate-hazards
are O
considered O
as O
some O
of O
the O
most O
disastrous O
hazards O
of O
the O
country O
and O
have O
resulted O
in O
huge O
number O
of O
deaths B-climate-impacts
and O
economic O
damages B-climate-impacts
in O
the O
past O
. O

During O
the O
years O
from O
1797 O
to O
2017 O
, O
there O
have O
been O
75 O
events O
of O
cyclone O
in O
the O
coastal O
areas O
of O
Bangladesh O
most O
of O
which O
were O
accompanied O
by O
storm B-climate-hazards
surges I-climate-hazards
. O

This O
study O
focuses O
on O
storm B-climate-hazards
surge I-climate-hazards
modelling O
for O
Patuakhali O
district O
of O
Bangladesh O
located O
in O
the O
south O
central O
part O
of O
Bangladesh O
coast O
. O

For O
this O
study O
, O
maximum O
cyclonic O
winds B-climate-nature
of O
5 O
, O
10 O
, O
20 O
, O
50 O
and O
100 O
years O
return O
periods O
were O
calculated O
using O
Gumbel O
distribution O
of O
annual B-climate-properties
maximum I-climate-properties
wind I-climate-properties
speeds I-climate-properties
in O
Bangladesh O
between O
1960 O
and O
2017 O
. O

A O
linear O
decay O
model O
namely O
the O
Surge B-climate-models
Decay I-climate-models
Coefficient I-climate-models
was O
used O
to O
calculate O
the O
surge B-climate-nature
decrease O
with O
distance B-climate-properties
from O
the O
coast O
. O

Spatial O
analysis O
of O
the O
surge B-climate-properties
height I-climate-properties
, O
linear O
decay O
model O
and O
Digital O
Elevation O
Model O
( O
DEM O
) O
are O
used O
to O
produce O
the O
final O
hazard O
maps O
. O

-DOCSTART- -X- O O 9542b576f635fe90cf1b8a95c9d4e67f

derived O
from O
satellite O
( O
SeaWiFS B-climate-observations
) O
sea B-climate-observations
- I-climate-observations
surface I-climate-observations
colour I-climate-observations
observations I-climate-observations
. O

-DOCSTART- -X- O O f9a37b3e79c8993ba2f8d569c318b68f

In O
this O
study O
, O
five O
open O
access O
gridded O
precipitation B-climate-nature
( O
GP O
) O
products O
( O
CFSR B-climate-models
, O
MSWEPv1.1 B-climate-datasets
, O
PERSIANN B-climate-datasets
- I-climate-datasets
CDR I-climate-datasets
, O
CMORPH B-climate-datasets
, O
and O
CHIRPSv2.0 B-climate-datasets
) O
and O
local O
climate O
data O
were O
evaluated O
over O
the O
Grande O
de O
San O
Miguel O
( O
GSM O
) O
River B-climate-nature
Basin I-climate-nature
in O
El O
Salvador O
. O

Secondly O
, O
the O
SWAT B-climate-models
model O
was O
used O
to O
simulate O
the O
streamflow B-climate-properties
regimen O
based O
on O
the O
precipitation B-climate-nature
datasets O
. O

-DOCSTART- -X- O O c6801c47715a759de925b0aed506f2ef

The O
impacts O
of O
land O
use O
on O
surface O
vegetation B-climate-nature
, O
radiative B-climate-nature
, O
and O
hydrological B-climate-nature
properties O
were O
evaluated O
using O
Landsat B-climate-observations
image O
- O
derived O
biophysical O
indices O
. O

-DOCSTART- -X- O O 373a3d9c5fab0d6cfe45f0b08ed9073f

This O
study O
employs O
sensitivity O
analysis O
on O
an O
emulated O
perturbed O
parameter O
ensemble O
of O
the O
global O
aerosol O
- O
climate O
model O
ECHAM B-climate-models
- I-climate-models
HAM I-climate-models
to O
illuminate O
the O
impact O
of O
selected O
CMP O
cloud B-climate-nature
ice I-climate-nature
processes I-climate-nature
on O
model O
output O
. O

-DOCSTART- -X- O O e5ce48dcee8b8303ee0cf585a999c317

The O
first O
experimental O
evidence O
that O
radio O
occultation O
data O
may O
have O
a O
positive O
impact O
on O
weather B-climate-nature
prediction O
systems O
was O
obtained O
from O
statistical O
comparisons O
of O
the O
GPS B-climate-observations
/ I-climate-observations
MET I-climate-observations
retrieved O
refractivity B-climate-properties
and O
temperature B-climate-properties
profiles O
to O
global O
Numerical O
Weather O
Prediction O
( O
NWP O
) O
model O
analysis O
. O

As O
a O
matter O
of O
fact O
, O
the O
discrimination O
of O
the O
water B-climate-properties
vapour I-climate-properties
quantities I-climate-properties
and O
of O
the O
temperature B-climate-properties
profiles O
is O
not O
possible O
in O
low B-climate-nature
atmosphere I-climate-nature
layers I-climate-nature
by O
the O
only O
analysis O
of O
GNSS B-climate-observations
signals I-climate-observations
. O

-DOCSTART- -X- O O 8affa1ac4795875c3448b80efcb2397f

This O
study O
provides O
an O
analysis O
of O
the O
impact O
of O
global O
climate B-climate-mitigations
policies I-climate-mitigations
on O
mercury B-climate-hazards
emissions I-climate-hazards
using O
the O
Greenhouse B-climate-models
Gas I-climate-models
and I-climate-models
Air I-climate-models
Pollution I-climate-models
Interactions I-climate-models
and I-climate-models
Synergies I-climate-models
( O
GAINS B-climate-models
) O
model O
in O
the O
time O
horizon O
up O
to O
2050 O
. O

The O
time O
evolution O
of O
mercury B-climate-hazards
emissions I-climate-hazards
is O
based O
on O
projections O
of O
energy O
consumption O
provided O
by O
the O
Prospective B-climate-models
Outlook I-climate-models
for I-climate-models
the I-climate-models
Long I-climate-models
term I-climate-models
Energy I-climate-models
System I-climate-models
( O
POLES B-climate-models
) O
model O
for O
a O
scenario O
without O
any O
global O
greenhouse O
gas O
mitigation O
efforts O
, O
and O
for O
a O
2 O
° O
C O
climate B-climate-mitigations
policy I-climate-mitigations
scenario O
, O
which O
assumes O
internationally O
coordinated O
action O
to O
mitigate O
climate O
change O
. O

-DOCSTART- -X- O O cd2a01425f53908abfb0a703cbbd6ec7

Abstract O
The O
Madden B-climate-nature
– I-climate-nature
Julian I-climate-nature
Oscillation I-climate-nature
( O
MJO B-climate-nature
Here O
we O
explore O
a O
non O
- O
linear O
classification O
method O
, O
the O
self O
- O
organizing O
map O
( O
SOM O
) O
, O
a O
type O
of O
artificial O
neural O
network O
used O
to O
produce O
a O
low O
- O
dimensional O
representation O
of O
high O
- O
dimensional O
datasets O
, O
to O
capture O
more O
accurately O
the O
life B-climate-assets
cycle O
of O
the O
MJO B-climate-nature
and O
its O
global O
impacts O
. O

-DOCSTART- -X- O O a4f40aa2c2fee49947e6ecec14be52c7

We O
analyzed O
the O
spatiotemporal O
evolution O
of O
drought B-climate-hazards
frequency O
in O
China O
at O
grid O
level O
during O
1981 O
- O
2019 O
with O
the O
nonparametric O
Mann O
- O
Kendall O
trend O
method O
, O
using O
a O
high O
temporal O
resolution O
vegetation O
health O
index O
dataset O
at O
week O
- O
scale O
. O

-DOCSTART- -X- O O 2dfebda6e02f88d0d1d7df6a0cbb4557

Building B-climate-assets
upon O
a O
recent O
examination O
of O
the O
univariate O
relationships O
between O
synoptic O
- O
scale O
weather O
patterns O
and O
water B-climate-properties
clarity I-climate-properties
, O
this O
research O
utilizes O
nonlinear O
autoregressive O
models O
with O
exogenous O
input O
( O
NARX O
models O
) O
to O
explore O
the O
multivariate O
climate O
- O
to O
- O
water O
clarity O
relationship O
. O

-DOCSTART- -X- O O 717f130d80cbcb1070976c2f6fc5996f

The O
effects O
of O
CCV O
are O
assessed O
in O
relation O
to O
the O
availability O
of O
irrigation B-climate-mitigations
water O
and O
the O
irrigation B-climate-mitigations
needs O
of O
maize B-climate-assets
. O

-DOCSTART- -X- O O 6627c661c76b7663112dc3d52bf05217

Extreme B-climate-hazards
climate I-climate-hazards
events I-climate-hazards
such O
as O
hurricanes B-climate-hazards
can O
influence O
the O
movement O
and O
distribution O
of O
fish B-climate-organisms
and O
other O
aquatic O
vertebrates O
. O

In O
this O
study O
, O
we O
used O
acoustic O
telemetry O
data O
to O
investigate O
the O
movement O
patterns O
of O
common B-climate-organisms
snook I-climate-organisms
( O
Centropomus B-climate-organisms
undecimalis I-climate-organisms
) O
in O
the O
Florida O
Coastal O
Everglades O
during O
Hurricane O
Irma O
, O
which O
made O
landfall O
on O
the O
southwest O
Florida O
coast O
as O
a O
Category O
3 O
storm B-climate-nature
on O
10 O
September O
2017 O
after O
passing O
in O
close O
proximity O
to O
our O
study O
site O
. O

-DOCSTART- -X- O O fceae3c44bc06c70723a444cb10ef4df

This O
study O
presents O
an O
analysis O
of O
surface B-climate-properties
albedo I-climate-properties
change O
over O
Greenland O
using O
a O
32 O
- O
year O
consistent O
satellite O
albedo B-climate-properties
product O
from O
the O
global B-climate-observations
land I-climate-observations
surface I-climate-observations
satellite I-climate-observations
( O
GLASS B-climate-observations
) O
project O
together O
with O
ground O
measurements O
. O

-DOCSTART- -X- O O f0ec42fff593d1c177659764292fed4f

With O
the O
help O
of O
a O
regional O
climate O
model O
, O
NCEP B-climate-models
re O
- O
analyses O
, O
spatially O
disaggregated O
lead B-climate-hazards
emissions O
from O
road B-climate-problem-origins
traffic I-climate-problem-origins
and O
point O
sources O
, O
and O
various O
local O
data O
, O
the O
airborne O
pathways O
and O
depositions O
of O
gasoline B-climate-hazards
lead I-climate-hazards
in O
Europe O
since O
1958 O
were O
reconstructed O
. O

-DOCSTART- -X- O O 14367299ea573bd9810d4b3db8bcb4b7

This O
paper O
applies O
Ricardian O
approach O
to O
measure O
the O
effect O
of O
climate O
change O
on O
agriculture B-climate-assets
performance O
in O
Togo O
using O
time O
series O
data O
from O
the O
period O
1971 O
- O
2004 O
. O

-DOCSTART- -X- O O 62776abd387f148e5a1e77e9adf97a67

This O
paper O
used O
three O
MARKAL B-climate-models
( O
MARKet B-climate-models
ALlocation I-climate-models
) O
family O
models O
, O
that O
is O
, O
MARKAL B-climate-models
, O
MARKAL B-climate-models
- I-climate-models
ED I-climate-models
( O
MARKAL B-climate-models
with I-climate-models
elastic I-climate-models
demand I-climate-models
) O
, O
and O
MARKAL B-climate-models
- I-climate-models
MACRO I-climate-models
, O
to O
study O
China O
energy O
system O
's O
carbon B-climate-mitigations
mitigation I-climate-mitigations
strategies I-climate-mitigations
and O
corresponding O
impacts O
on O
the O
economy O
. O

-DOCSTART- -X- O O 7b21f019c612cd87e6a0c7c84a469fdf

The O
Geneva O
Canton O
( O
Switzerland O
) O
promotes O
a O
stormwater B-climate-mitigations
management I-climate-mitigations
policy I-climate-mitigations
at O
the O
plot O
scale O
aiming O
at O
better O
controlling O
/ O
mastering O
the O
floods B-climate-hazards
in O
densely O
urbanised O
areas O
. O

-DOCSTART- -X- O O 3cda772cf79dffe26f6638b0a77c60e7

Input O
climate O
data O
pertained O
to O
a O
reference O
period O
and O
SRES B-climate-datasets
climate O
scenarios O
A1B I-climate-datasets
, I-climate-datasets
A2 I-climate-datasets
and O
RCP B-climate-datasets
8.5 I-climate-datasets
applied O
to O
three O
characteristic O
climatic O
areas O
in O
Bosnia O
and O
Herzegovina O
. O

-DOCSTART- -X- O O b1850692599586cbcc3dfbd77d692c7f

In O
this O
paper O
a O
new O
empirical O
model O
( O
‘ O
Temperature B-climate-models
I15 I-climate-models
’ O
model O
) O
was O
developed O
to O
predict O
the O
daily O
maximum B-climate-properties
15 I-climate-properties
- I-climate-properties
min I-climate-properties
rainfall I-climate-properties
intensity I-climate-properties
( O
I15 B-climate-properties
) O
using O
daily O
minimum O
and O
maximum B-climate-properties
temperature I-climate-properties
and O
daily O
rainfall B-climate-properties
totals I-climate-properties
from O
12 O
selected O
pluviograph O
stations O
across O
Australia O
. O

The O
new O
‘ O
Temperature B-climate-models
I15 I-climate-models
’ O
model O
was O
implemented O
in O
the O
runoff B-climate-nature
equation O
of O
the O
Australia O
- O
wide O
spatial O
pasture O
growth O
model O
AussieGRASS B-climate-models
, O
which O
predicts O
daily O
water O
balance O
and O
pasture B-climate-assets
growth O
for O
185 O
different O
pasture B-climate-assets
communities O
. O

-DOCSTART- -X- O O 643aeb6c88b4e2fa6a2a06fe06c204df

Structural O
equation O
models O
confirmed O
these O
results O
, O
but O
also O
indicated O
that O
effects O
of O
climate O
warming O
on O
nematodes B-climate-organisms
were O
indirect O
primarily O
through O
shifts O
in O
plant B-climate-organisms
and O
microbial B-climate-organisms
communities I-climate-organisms
and O
changes O
of O
soil B-climate-nature
water I-climate-nature
holding O
capacity O
. O

-DOCSTART- -X- O O 7e95f3a149f1ecb0c4d28068ae4a10c7

Using O
history O
climate O
data O
and O
two O
representative O
climate O
change O
scenarios O
, O
we O
predicted O
the O
potential O
distribution O
of O
bamboo B-climate-assets
in O
China O
from O
1961 O
to O
2099 O
based O
on O
specie B-climate-organisms
distribution I-climate-organisms
models O
. O

-DOCSTART- -X- O O 33080ec684926d37878a2b86c8454707

Projected O
changes O
of O
precipitation B-climate-nature
and O
temperature B-climate-properties
, O
derived O
from O
an O
ensemble O
of O
4 O
climate O
model O
( O
CM O
) O
runs O
for O
the O
period O
2040 O
- O
2070 O
under O
the O
SRES B-climate-datasets
A1B I-climate-datasets
emission B-climate-problem-origins
scenario O
, O
have O
been O
downscaled O
and O
bias O
corrected O
before O
using O
them O
as O
climatic O
forcing O
in O
a O
hydrological B-climate-nature
model O
. O

-DOCSTART- -X- O O a39746fe890c2d28d30623176d6fd047

To O
this O
end O
, O
this O
study O
employs O
the O
state O
- O
of O
- O
the O
- O
art O
global O
integrated O
assessment O
model O
MESSAGEix B-climate-models
- I-climate-models
GLOBIOM I-climate-models
to O
investigate O
mid O
- O
century O
decarbonization B-climate-mitigations
strategies I-climate-mitigations
for O
developing O
Asia O
to O
2050 O
. O

-DOCSTART- -X- O O 5829d62109cb2278cba2558af85a2747

Innovative B-climate-mitigations
financing I-climate-mitigations
models I-climate-mitigations
are O
emerging O
globally O
to O
advance O
nature B-climate-mitigations
- I-climate-mitigations
based I-climate-mitigations
solutions I-climate-mitigations
( O
NBS B-climate-mitigations
) O
that O
can O
cost O
- O
effectively O
enhance O
infrastructure B-climate-assets
performance O
, O
meet O
Sustainable B-climate-mitigations
Development I-climate-mitigations
Goals I-climate-mitigations
, O
and O
mitigate O
the O
negative O
impacts O
of O
climate O
change O
. O

-DOCSTART- -X- O O 317ca0cecdf88be3df4e2320340f6e37

A O
spatially O
explicit O
model O
( O
MIGRATE B-climate-models
) O
was O
used O
to O
investigate O
the O
effects O
of O
habitat B-climate-hazards
loss I-climate-hazards
and O
fragmentation B-climate-hazards
on O
the O
ability O
of O
species B-climate-organisms
to O
migrate O
in O
response O
to O
climate O
change O
. O

Illustrative O
simulations O
were O
run O
using O
parameters O
that O
represent O
the O
reproductive O
and O
dispersal O
characteristics O
of O
the O
wind O
- O
dispersed O
tree B-climate-organisms
Tilia B-climate-organisms
cordata I-climate-organisms
( O
small B-climate-organisms
- I-climate-organisms
leaved I-climate-organisms
lime I-climate-organisms
) O
. O

-DOCSTART- -X- O O 2ff860d6428fbac169f401f66a6966bf

To O
study O
near B-climate-nature
- I-climate-nature
surface I-climate-nature
melt I-climate-nature
changes I-climate-nature
over O
the O
Greenland O
ice O
sheet O
( O
GrIS O
) O
since O
1979 O
, O
melt B-climate-nature
extent I-climate-nature
estimates O
from O
two O
regional O
climate O
models O
were O
compared O
with O
those O
obtained O
from O
spaceborne O
microwave O
brightness O
temperatures O
using O
two O
different O
remote O
sensing O
algorithms O
. O

-DOCSTART- -X- O O b1ecf989ff8a1e7a31d00966d18ad1f7

The O
GEOS B-climate-models
- I-climate-models
Chem I-climate-models
model O
shows O
that O
the O
mercury B-climate-hazards
concentrations I-climate-hazards
for O
all O
tracers O
( O
1 O
to O
3 O
) O
, O
elemental O
mercury O
( O
Hg(0 O
) O
) O
, O
divalent O
mercury O
( O
Hg(II O
) O
) O
and O
primary O
particulate O
mercury O
( O
Hg(P O
) O
) O
have O
differences O
between O
2000 O
and O
2050 O
in O
most O
regions O
over O
the O
world O
. O

-DOCSTART- -X- O O a0e46ab4c2e33f109b728aea1f69802f

MOPITT B-climate-datasets
( O
Measurement B-climate-datasets
Of I-climate-datasets
Pollution I-climate-datasets
In I-climate-datasets
The I-climate-datasets
Troposphere I-climate-datasets
) O
satellite O
data O
have O
been O
assimilated O
in O
a O
tropospheric B-climate-nature
Chemistry B-climate-models
Transport I-climate-models
Model I-climate-models
( O
CTM B-climate-models
) O
to O
obtain O
global O
fields O
of O
CO O
. O

MOPITT B-climate-datasets
data O
will O
be O
also O
used O
to O
invert O
sources O
of O
CO O
and O
CH B-climate-greenhouse-gases
4 I-climate-greenhouse-gases
by O
data O
assimilation O
techniques O
. O

-DOCSTART- -X- O O 91b08cb5ffb7adeba89995f0cdf7329f

The O
model O
is O
based O
on O
Powersim B-climate-models
, O
a O
dynamic O
simulation O
software O
package O
capable O
of O
producing O
web O
- O
accessible O
, O
intuitive O
, O
graphic O
, O
user O
- O
friendly O
interfaces O
. O

The O
model O
is O
used O
in O
a O
University B-climate-organizations
of I-climate-organizations
Arizona I-climate-organizations
undergraduate O
class O
and O
within O
the O
Arizona B-climate-organizations
Master I-climate-organizations
Watershed I-climate-organizations
Stewards I-climate-organizations
Program I-climate-organizations
. O

-DOCSTART- -X- O O c408ccb10cfaef455d0adf98f1cd27ef

No O
previous O
models O
of O
the O
Ganga O
Basin O
integrate O
all O
these O
aspects O
, O
and O
this O
is O
the O
first O
time O
that O
a O
participatory O
approach O
was O
applied O
for O
the O
development O
of O
a O
Ganga O
Basin O
model O
. O

-DOCSTART- -X- O O 66e0d7193ee798114a7e5308c5fa64ef

A O
Lagrangian O
dispersion O
model O
is O
used O
in O
combination O
with O
a O
mean O
emission B-climate-problem-origins
inventory O
and O
emissions B-climate-problem-origins
along O
actual O
flight B-climate-problem-origins
tracks O
. O

Both O
inventories O
are O
established O
from O
Measurement B-climate-datasets
of I-climate-datasets
Ozone I-climate-datasets
and I-climate-datasets
Water I-climate-datasets
Vapor I-climate-datasets
by I-climate-datasets
Airbus I-climate-datasets
In I-climate-datasets
- I-climate-datasets
Service I-climate-datasets
Aircraft I-climate-datasets
( O
MOZAIC B-climate-datasets
) O
aircraft O
position O
data O
in O
the O
North O
Atlantic O
Flight O
Corridor O
( O
NAFC O
) O
over O
a O
1 O
- O
year O
period O
. O

-DOCSTART- -X- O O 9fedc676682994d5088993b7c468cdbf

In O
this O
paper O
, O
we O
use O
an O
observational O
dataset O
built O
from O
Argo B-climate-observations
in I-climate-observations
situ I-climate-observations
profiles I-climate-observations
to O
describe O
the O
main O
large O
- O
scale O
patterns O
of O
intraseasonal O
mixed B-climate-properties
layer I-climate-properties
depth I-climate-properties
( O
MLD B-climate-properties
) O
variations O
in O
the O
Indian O
Ocean O
. O

-DOCSTART- -X- O O 15ae570a692988930d4dc05267b1198f

For O
this O
purpose O
, O
we O
construct O
a O
close O
relative O
of O
the O
DICE B-climate-models
model O
in O
a O
recursive O
dynamic O
programming O
framework O
. O

We O
analyze O
different O
ways O
how O
damage B-climate-impacts
uncertainty O
can O
affect O
the O
DICE B-climate-models
equations O
. O

-DOCSTART- -X- O O 7843f838b7cffae0ba958c23f1af87a7

The O
model O
is O
based O
on O
the O
underlying O
assumptions O
of O
the O
so O
- O
called O
RAINS B-climate-models
model O
frequently O
used O
to O
assess O
the O
potential O
and O
the O
costs O
for O
reducing O
air B-climate-hazards
pollution I-climate-hazards
in O
Europe O
. O

The O
results O
also O
indicate O
that O
although O
it O
is O
clear O
that O
the O
Eastern O
European O
countries O
are O
not O
homogenous O
in O
terms O
of O
CO2 B-climate-greenhouse-gases
abatement O
potential O
and O
costs O
, O
no O
single O
country O
emerges O
as O
particularly O
low O
cost O
. O

-DOCSTART- -X- O O 0ced67e688d09ba8859cebb1c128935f

Changes O
in O
climate B-climate-hazards
extremes I-climate-hazards
were O
detected O
by O
Expert B-climate-organizations
Team I-climate-organizations
on I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
Detection I-climate-organizations
and I-climate-organizations
Indices I-climate-organizations
( O
ETCCDI B-climate-organizations
) O
. O

-DOCSTART- -X- O O 36527d77b9d936a01b349e58f91f0b87

GPS B-climate-observations
data O
have O
been O
collected O
during O
the O
grazing B-climate-assets
season O
in O
2010 O
. O

ArcGIS B-climate-models
package O
was O
used O
for O
data O
storage O
, O
management O
, O
visualization O
and O
analysis O
. O

-DOCSTART- -X- O O 8683fa38b15d0d70f86acadbd556051f

Changes O
in O
climate O
and O
land B-climate-problem-origins
use I-climate-problem-origins
land I-climate-problem-origins
cover I-climate-problem-origins
( O
LULC B-climate-problem-origins
) O
are O
important O
factors O
that O
affect O
water B-climate-properties
yield I-climate-properties
( O
WY B-climate-properties
) O
. O

The O
objectives O
of O
the O
study O
were O
: O
( O
1 O
) O
To O
estimate O
a O
water B-climate-properties
yield I-climate-properties
model O
using O
integrated B-climate-models
valuation I-climate-models
of I-climate-models
ecosystem I-climate-models
services I-climate-models
and I-climate-models
tradeoffs I-climate-models
( O
InVEST B-climate-models
) O
, O
and O
( O
2 O
) O
to O
test O
the O
sensitivity O
of O
water B-climate-properties
yield I-climate-properties
( O
WY B-climate-properties
) O
to O
changes O
in O
climate O
variables O
( O
rainfall B-climate-nature
and O
evapotranspiration B-climate-nature
) O
and O
in O
LULC B-climate-properties
. O

The O
integration O
of O
remote O
sensing O
( O
RS O
) O
, O
geographic O
information O
system O
( O
GIS O
) O
, O
and O
the O
integrated B-climate-models
valuation I-climate-models
of I-climate-models
ecosystem I-climate-models
services I-climate-models
and I-climate-models
tradeoffs I-climate-models
( O
InVEST B-climate-models
) O
approach O
were O
used O
in O
this O
study O
. O

-DOCSTART- -X- O O 92b62230997d6be9c63ddc04672899bf

TRMM B-climate-datasets
Multi I-climate-datasets
- I-climate-datasets
satellite I-climate-datasets
Precipitation I-climate-datasets
Analysis I-climate-datasets
( O
TMPA B-climate-datasets
) O
satellite O
precipitation O
products O
have O
been O
utilized O
to O
quantify O
, O
forecast O
, O
or O
understand O
precipitation B-climate-nature
patterns O
, O
climate O
change O
, O
hydrologic B-climate-nature
models O
, O
and O
drought B-climate-hazards
in O
numerous O
scientific O
investigations O
. O

In O
this O
study O
, O
we O
evaluated O
four O
TMPA B-climate-datasets
products O
( O
3B42 B-climate-datasets
: I-climate-datasets
V6 I-climate-datasets
, I-climate-datasets
V7temp I-climate-datasets
, I-climate-datasets
V7 I-climate-datasets
, I-climate-datasets
RTV7 I-climate-datasets
) O
against O
125 O
rain O
gauges O
in O
Northern O
Morocco O
to O
assess O
the O
accuracy O
of O
TMPA B-climate-datasets
products O
in O
various O
regimes O
, O
examine O
the O
performance O
metrics O
of O
new O
algorithm O
developments O
, O
and O
assess O
the O
impact O
of O
the O
processing O
error O
in O
2012 O
. O

-DOCSTART- -X- O O 5057e657eee7a8e6fd219fa204526a37

A O
new O
sunflower B-climate-assets
module O
was O
released O
in O
the O
Agricultural B-climate-models
Production I-climate-models
systems I-climate-models
SIMulator I-climate-models
APSIM B-climate-models
version I-climate-models
2.0 I-climate-models
in O
February O
2001 O
. O

-DOCSTART- -X- O O e3535b08b25a6d0ce77999d5fc32975f

In O
this O
paper O
, O
a O
Soil B-climate-models
and I-climate-models
Water I-climate-models
Assessment I-climate-models
Tool I-climate-models
( O
SWAT B-climate-models
) O
model O
of O
the O
Niger O
River O
watershed B-climate-nature
at O
Koulikoro O
was O
successfully O
calibrated O
, O
then O
forced O
with O
the O
climate O
time O
series O
of O
variable O
length O
generated O
by O
nine O
regional O
climate O
models O
( O
RCMs O
) O
from O
the O
AMMA B-climate-models
- I-climate-models
ENSEMBLES I-climate-models
experiment O
. O

The O
RCMs O
were O
run O
under O
the O
SRES B-climate-datasets
A1B I-climate-datasets
emissions B-climate-problem-origins
scenario O
. O

-DOCSTART- -X- O O f15832ccb26ce7fdf2d64efe05f2b9af

For O
the O
purpose O
of O
simulation O
, O
a O
LEED B-climate-mitigations
Gold I-climate-mitigations
certified I-climate-mitigations
building B-climate-assets
located O
at O
a O
major O
university O
in O
the O
U.S. O
was O
modeled O
, O
benchmarked O
, O
and O
calibrated O
. O

-DOCSTART- -X- O O 8f7751ae613e7f037c545ff3ba5666af

In O
this O
study O
, O
we O
used O
a O
regional O
climate O
model O
, O
the O
Weather B-climate-models
Research I-climate-models
and I-climate-models
Forecasting I-climate-models
Model I-climate-models
with I-climate-models
Chemistry I-climate-models
( O
WRF B-climate-models
- I-climate-models
Chem I-climate-models
) O
to O
project O
air B-climate-hazards
pollution I-climate-hazards
in O
NC O
and O
investigate O
the O
variations O
of O
air B-climate-hazards
pollutions I-climate-hazards
response O
to O
future O
climate O
changes O
, O
which O
probably O
has O
an O
implication O
to O
strategy O
and O
control O
policy O
for O
air B-climate-assets
quality I-climate-assets
in O
NC O
. O

A O
comprehensive O
model O
evaluation O
was O
conducted O
to O
verify O
the O
simulated O
aerosol B-climate-nature
optical B-climate-properties
depth I-climate-properties
( O
AOD B-climate-properties
) O
based O
on O
MODIS B-climate-observations
and O
MISR B-climate-observations
datasets O
, O
and O
the O
model O
also O
showed O
reasonable O
results O
in O
aerosol B-climate-nature
concentrations O
. O

-DOCSTART- -X- O O 5d7b292768262c32ad2076877bd557f7

The O
objective O
of O
this O
study O
was O
to O
determine O
optimum O
irrigation B-climate-mitigations
termination O
periods O
for O
cotton B-climate-assets
production O
in O
the O
THP O
under O
full O
and O
deficit O
irrigation B-climate-mitigations
conditions O
using O
the O
Decision B-climate-models
Support I-climate-models
System I-climate-models
for I-climate-models
Agrotechnology I-climate-models
Transfer I-climate-models
( O
DSSAT B-climate-models
) O
CROPGRO B-climate-models
- I-climate-models
Cotton I-climate-models
model O
, O
which O
was O
evaluated O
in O
a O
prior O
study O
in O
the O
THP O
using O
measured O
data O
from O
an O
IWUE B-climate-properties
field O
experiment O
at O
Halfway O
. O

-DOCSTART- -X- O O 3c0d42a28f607e8af2454725edc7f61f

We O
assess O
the O
convergence O
of O
PFASST B-climate-models
- I-climate-models
SH I-climate-models
upon O
refinement O
in O
time O
to O
investigate O
the O
impact O
of O
the O
coarsening O
strategy O
on O
the O
accuracy O
of O
the O
scheme O
, O
and O
specifically O
on O
its O
ability O
to O
capture O
the O
high O
- O
frequency O
modes O
accumulating O
in O
the O
solution O
. O

-DOCSTART- -X- O O >>> bpf TEST DATA END, SEPTEMBER

-DOCSTART- -X- O O >>> Now data from "test", to be used for training only

-DOCSTART- -X- O O 4ac7512da5cf031a28054a88c5499def

Large O
areas O
of O
trees B-climate-organisms
are O
being O
planted O
in O
Australian O
agricultural B-climate-assets
lands I-climate-assets
for O
a O
range O
of O
environmental O
, O
ecological O
and O
economic O
benefits O
. O

This O
thesis O
develops O
and O
applies O
methods O
to O
quantify O
the O
benefits O
of O
woodlots B-climate-nature
such O
as O
habitat B-climate-organisms
for O
woodland B-climate-organisms
birds I-climate-organisms
, O
timber B-climate-assets
production O
, O
shelter O
for O
neighbouring O
agriculture O
and O
reduction O
in O
deep O
drainage O
. O

-DOCSTART- -X- O O 6a1689cde229b69c8eaee612ae437ad7

ambient B-climate-properties
temperatures I-climate-properties
will O
increase O
heat B-climate-hazards
stress I-climate-hazards
on O
workers O
, O
leading O
to O
impacts O
upon O
their O
individual O
health B-climate-assets
and O
productivity B-climate-assets
. O

In O
particular O
, O
research O
has O
indicated O
that O
higher O
ambient B-climate-properties
temperatures I-climate-properties
can O
increase O
the O
prevalence O
of O
urolithiasis B-climate-impacts
. O

-DOCSTART- -X- O O 08c37f55de4c35b8c30ad9cac4986507

The O
mean O
particle B-climate-properties
infiltration I-climate-properties
ratio I-climate-properties
in O
Atlanta O
was O
0.70 O
± O
0.30 O
, O
with O
a O
0.21 O
lower O
ratio O
in O
summer O
compared O
to O
transition O
seasons O
( O
spring O
, O
fall O
) O
. O

Particle B-climate-properties
infiltration I-climate-properties
ratios I-climate-properties
were O
0.19 O
lower O
in O
houses O
using O
heating B-climate-problem-origins
, O
ventilation O
, O
and O
air B-climate-problem-origins
conditioning I-climate-problem-origins
( O
HVAC B-climate-problem-origins
) O
systems O
compared O
to O
those O
not O
using O
HVAC B-climate-problem-origins
. O

-DOCSTART- -X- O O 4971421cd6d265539457db450c2ddeaf

The O
potential O
impact O
of O
a O
changed O
climate O
on O
river B-climate-nature
basin I-climate-nature
runoff B-climate-nature
in O
mountainous B-climate-nature
regions O
in O
Slovakia O
was O
evaluated O
using O
a O
simulation O
of O
the O
runoff B-climate-nature
changes O
, O
carried O
out O
using O
a O
monthly O
time O
step O
. O

-DOCSTART- -X- O O ba37c4644538b08e3f16df5ea79ba4b7

One O
element O
of O
a O
large O
telescope O
corrector O
might O
be O
a O
one O
meter O
diameter B-climate-properties
asphere O
0.2 O
m O
thick B-climate-properties
and O
, O
with O
stringent O
homogeneity O
and O
surface O
requirements O
for O
the O
glass O
, O
represents O
a O
~ O
$ O
1 O
M O
investment O
. O

The O
index B-climate-properties
of I-climate-properties
refraction I-climate-properties
of O
air O
at O
sea B-climate-properties
level I-climate-properties
under O
standard O
conditions O
is O
n O
≈ O
1.003 O
. O

Depending O
upon O
the O
altitude B-climate-properties
of O
the O
observatory O
, O
telescopes O
look O
through O
~ O
100 O
km O
of O
atmosphere B-climate-nature
so O
the O
optical B-climate-properties
path I-climate-properties
difference I-climate-properties
( O
OPD B-climate-properties
) O
of O
the O
atmosphere B-climate-nature
is O
one O
to O
two O
meters O
. O

-DOCSTART- -X- O O 37d8a4565a70a94c1c09fd5c7b2c008f

global B-climate-properties
air I-climate-properties
temperatures I-climate-properties
continue O
to O
rise O
in O
response O
to O
climate O
change O
, O
environmental O
conditions O
for O
many O
freshwater B-climate-organisms
fish I-climate-organisms
species O
will O
change O
. O

Warming O
air B-climate-properties
temperatures I-climate-properties
may O
lead O
to O
warming O
lake B-climate-properties
temperatures I-climate-properties
, O
and O
subsequently O
, O
the O
availability O
of O
suitable O
thermal O
habitat B-climate-organisms
space O
. O

-DOCSTART- -X- O O 2305723ba6c6d1095da58fcbfa89712f

Malaysia O
has O
made O
a O
pledge O
to O
reduce O
its O
2005 O
GDP B-climate-properties
emission I-climate-properties
intensity I-climate-properties
levels O
by O
up O
to O
40 O
% O
by O
2020 O
as O
its O
contribution O
to O
combat O
climate O
change O
. O

One O
of O
the O
proposed O
policies O
to O
achieve O
this O
goal O
is O
carbon B-climate-mitigations
taxation I-climate-mitigations
. O

The O
Malaysian O
climate B-climate-mitigations
policy I-climate-mitigations
implies O
a O
gain O
on O
the O
Malaysian O
economy O
of O
around O
0.8 O
% O
of O
GDP B-climate-properties
. O

-DOCSTART- -X- O O dd6132fc93f207bab70b965564769477

Aim O
To O
quantify O
the O
consequences O
of O
major O
threats O
to O
biodiversity B-climate-organisms
, O
such O
as O
climate O
and O
land B-climate-mitigations
- I-climate-mitigations
use I-climate-mitigations
change I-climate-mitigations
, O
it O
is O
important O
to O
use O
explicit O
measures O
of O
species B-climate-organisms
persistence O
, O
such O
as O
extinction B-climate-hazards
risk O
. O

We O
evaluated O
the O
extinction B-climate-hazards
risk O
of O
three O
species B-climate-organisms
under O
different O
climate O
change O
scenarios O
in O
three O
different O
regions O
of O
the O
Mexican O
cloud B-climate-nature
forest I-climate-nature
, O
a O
highly O
fragmented O
habitat B-climate-organisms
that O
is O
particularly O
vulnerable O
to O
climate O
change O
. O

-DOCSTART- -X- O O 6dfb787089ec7c0c24ffc733f48247af

Geoengineering O
can O
be O
defined O
as O
the O
technologies O
that O
aim O
to O
deliberately O
alter O
geophysical O
mechanisms O
in O
order O
to O
alleviate O
the O
impacts O
of O
climate O
change O
. O

This O
paper O
studies O
the O
potential O
benefits O
from O
geoengineering O
in O
a O
standard O
one O
- O
sector O
growth O
model O
augmented O
with O
a O
carbon B-climate-nature
cycle I-climate-nature
and O
a O
climate O
system O
. O

-DOCSTART- -X- O O d83c30e1a831fe8ca12c093e54ce0e4f

Population B-climate-properties
, O
GDP B-climate-properties
, O
crude B-climate-properties
oil I-climate-properties
reserve I-climate-properties
, O
air B-climate-properties
temperature I-climate-properties
, O
and O
precipitation B-climate-nature
are O
influential O
factors O
for O
total B-climate-properties
water I-climate-properties
demand I-climate-properties
. O

The O
model O
helps O
address O
the O
dynamics O
of O
sustainability O
challenges O
, O
trade O
- O
offs O
, O
and O
synergies O
in O
the O
WEF B-climate-organizations
systems O
under O
drivers O
, O
such O
as O
climate O
change O
, O
population B-climate-problem-origins
growth I-climate-problem-origins
, O
economic O
development O
, O
and O
adaptation O
and O
resilience O
strategies O
and O
policies O
. O

-DOCSTART- -X- O O abff89c8da99324aa107382c02277857

Here O
we O
outline O
a O
framework O
and O
preliminary O
examples O
of O
applying O
these O
model O
outputs O
to O
the O
broad O
spectrum O
of O
fields O
associated O
with O
biodiversity B-climate-organisms
in O
the O
midlands O
region O
of O
Tasmania O
. O

-DOCSTART- -X- O O >>> bpf STOPPED HERE AGAIN TO UPDATE DICTS (mostly), TOO SLOW.

-DOCSTART- -X- O O 36ee755d3e65a51d843ada89d2a95527

Future O
climate O
and O
land B-climate-problem-origins
use I-climate-problem-origins
changes I-climate-problem-origins
will O
have O
implications O
for O
water B-climate-assets
quantity I-climate-assets
and I-climate-assets
quality I-climate-assets
and O
could O
make O
attainment O
of O
targets O
, O
such O
as O
those O
set O
by O
the O
EU B-climate-mitigations
Water I-climate-mitigations
Framework I-climate-mitigations
Directive I-climate-mitigations
, O
harder O
to O
achieve O
. O

For O
the O
climate O
component O
, O
net O
changes O
in O
mean B-climate-properties
annual I-climate-properties
runoff I-climate-properties
and O
seasonal B-climate-properties
precipitation I-climate-properties
were O
identified O
as O
the O
principal O
drivers O
affecting O
pollutant B-climate-hazards
availability O
and O
transport O
. O

A O
range O
of O
different O
pollutants B-climate-hazards
was O
considered O
, O
including O
N O
, O
P O
, O
C O
, O
suspended B-climate-hazards
solids I-climate-hazards
, O
pesticides B-climate-hazards
and O
faecal B-climate-hazards
indicator I-climate-hazards
organisms I-climate-hazards
. O

An O
example O
set O
of O
change O
scenarios O
was O
developed O
for O
Scotland O
based O
on O
data O
from O
the O
UKCP09 B-climate-models
climate I-climate-models
projections I-climate-models
and O
land B-climate-problem-origins
use I-climate-problem-origins
changes I-climate-problem-origins
linked O
to O
the O
Scottish O
Government O
's O
Land O
Use O
Strategy O
. O

-DOCSTART- -X- O O ddc396efb26381de1f2c2a23fd3d613f

The O
directionality O
of O
the O
direct O
solar B-climate-nature
beam I-climate-nature
introduces O
an O
asymmetry O
in O
the O
atmospheric B-climate-nature
heating I-climate-nature
of O
the O
convective B-climate-nature
motion I-climate-nature
and O
tilts O
the O
updraft O
. O

-DOCSTART- -X- O O 1d716a7ab56ba55950a444048dd83bc7

Mali O
, O
agricultural B-climate-assets
production O
systems O
are O
highly O
dependent O
on O
rainfall B-climate-nature
, O
which O
explains O
the O
direct O
impact O
of O
climate O
variability O
on O
food B-climate-assets
security I-climate-assets
and O
livelihoods B-climate-assets
of O
agricultural B-climate-assets
households I-climate-assets
. O

This O
study O
aims O
to O
analyze O
the O
determinants O
of O
FMNR B-climate-mitigations
adoption O
in O
the O
areas O
of O
Diema O
and O
Kolokani O
, O
in O
Mali O
. O

Results O
estimation O
from O
the O
Logit O
model O
show O
that O
FMNR B-climate-mitigations
training O
, O
harvesting O
, O
fertilizer B-climate-assets
utilization O
, O
perceptions O
of O
field O
fertility O
and O
income B-climate-assets
increase O
the O
probability O
of O
FMNR B-climate-mitigations
adoption O
. O

-DOCSTART- -X- O O 6514ac1d83aec2ecb03ffb78ebc70b57

Finally O
, O
a O
brief O
case O
study O
on O
direct B-climate-mitigations
evaporative I-climate-mitigations
cooling I-climate-mitigations
thermal O
performance O
and O
environmental O
impact O
was O
conducted O
as O
part O
of O
a O
field O
trip O
study O
conducted O
on O
an O
existing O
large O
scale O
installation O
in O
Mina O
Valley O
, O
Saudi O
Arabia O
. O

It O
was O
found O
that O
the O
evaporative B-climate-mitigations
cooling I-climate-mitigations
systems O
used O
for O
space O
cooling O
in O
pilgrims O
’ O
accommodations O
and O
in O
train O
stations O
could O
reduce O
energy O
consumption O
by O
as O
much O
as O
75 O
% O
and O
cut O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
emission B-climate-problem-origins
by O
78 O
% O
compared O
to O
traditional O
vapour B-climate-problem-origins
compression I-climate-problem-origins
systems I-climate-problem-origins
. O

-DOCSTART- -X- O O e89e281b44eeff66442c949ad4a2a1e7

Within O
Joshua O
Tree O
National O
Park O
( O
JTNP O
) O
, O
Joshua B-climate-organisms
trees I-climate-organisms
( O
Yucca B-climate-organisms
brevifolia I-climate-organisms
) O
reach O
their O
southern O
- O
most O
distribution O
. O

Previous O
research O
modeling O
distributional O
shifts O
of O
Joshua B-climate-organisms
trees I-climate-organisms
in O
response O
to O
climate O
change O
have O
been O
conducted O
at O
large O
regional O
scales O
, O
predicting O
widespread O
extirpation B-climate-hazards
of O
Joshua B-climate-organisms
trees I-climate-organisms
from O
their O
current O
southern O
and O
central O
distribution O
. O

Here O
we O
employed O
the O
Mahalanobis O
D2 O
statistic O
and O
constructed O
a O
finer O
- O
scale O
model O
of O
the O
Joshua B-climate-organisms
tree I-climate-organisms
’s O
current O
distribution O
within O
and O
surrounding O
JTNP O
, O
and O
then O
assessed O
their O
sensitivity O
to O
a O
gradient O
of O
climate O
change O
scenarios O
. O

-DOCSTART- -X- O O a82613ae4d5ea6dbe73cd9e5cf9ca147

The O
project O
also O
carried O
out O
on O
- O
ground O
surveys O
from O
cotton B-climate-assets
and O
sugar B-climate-assets
industry I-climate-assets
and O
conducted O
modelling O
to O
assess O
risks O
and O
the O
role O
of O
insurance B-climate-mitigations
for O
cotton B-climate-assets
and O
sugar B-climate-assets
cane I-climate-assets
farmers O
in O
Queensland O
. O

-DOCSTART- -X- O O 63546017db5530cfda3a3b2be2c52ef7

This O
study O
aims O
to O
assess O
the O
effects O
of O
climate O
variability O
on O
maize B-climate-assets
yield O
in O
Tanzania O
. O

Regression O
, O
co O
integration O
and O
ARIMA O
models O
were O
applied O
. O

The O
regression O
results O
showed O
that O
, O
April O
monthly B-climate-properties
rainfall I-climate-properties
for O
Arusha O
was O
significant O
( O
0.0027 O
) O
. O

There O
are O
significant O
positive O
long O
run O
relationships O
between O
maize B-climate-assets
yield O
and O
rainfall B-climate-nature
for O
Arusha O
, O
Dodoma O
, O
Songea O
, O
Tabora O
and O
Musoma O
districts O
. O

-DOCSTART- -X- O O 500a8d8440f9876c1c245cf6272280cf

It O
catalogues O
the O
impact O
of O
climate O
change O
and O
environmental O
degradation O
ranging O
from O
drought B-climate-hazards
in O
the O
Amazon O
to O
floods B-climate-hazards
in O
Haiti O
and O
elsewhere O
; O
vanishing B-climate-hazards
glaciers I-climate-hazards
in O
Colombia O
to O
extreme B-climate-hazards
cold I-climate-hazards
in O
the O
Andes O
; O
and O
hurricanes B-climate-hazards
, O
not O
only O
in O
Central O
America O
and O
the O
Caribbean O
, O
but O
also O
in O
southern O
Brazil O
. O

Across O
the O
region O
the O
capacity O
of O
natural O
ecosystems O
to O
act O
as O
buffers O
against O
extreme B-climate-hazards
weather I-climate-hazards
events O
and O
other O
shocks O
is O
being O
undermined O
leaving O
people B-climate-assets
more O
vulnerable O
. O

-DOCSTART- -X- O O dae0fb9cd1904a3d6681df1c3fb1a57f

Carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
emissions B-climate-problem-origins
from O
electricity B-climate-problem-origins
generation I-climate-problem-origins
are O
a O
major O
cause O
of O
anthropogenic O
climate O
change O
. O

The O
deployment O
of O
wind B-climate-mitigations
and I-climate-mitigations
solar I-climate-mitigations
power I-climate-mitigations
reduces O
these O
emissions B-climate-problem-origins
, O
but O
is O
subject O
to O
the O
variability O
of O
the O
weather O
. O

-DOCSTART- -X- O O 94fcd9f9f3b67a4b5659947f5db348cf

Climate B-climate-mitigations
- I-climate-mitigations
smart I-climate-mitigations
agriculture I-climate-mitigations
is O
a O
site O
- O
specific O
and O
knowledge O
intensive O
concept O
that O
urges O
the O
world O
to O
pay O
attention O
to O
impacts O
of O
climate O
change O
on O
agriculture B-climate-assets
and O
food B-climate-assets
security I-climate-assets
by O
promoting O
sustainable O
practices O
that O
will O
increase O
agricultural B-climate-assets
productivity I-climate-assets
and O
build O
resilience O
to O
environmental O
pressures O
including O
adaptation O
to O
climate O
change O
. O

The O
need O
to O
meet O
food B-climate-assets
demands O
that O
support O
poor O
small B-climate-assets
- I-climate-assets
scale I-climate-assets
agricultural I-climate-assets
producers O
and O
enhance O
national O
food B-climate-assets
security I-climate-assets
and O
development O
goals O
is O
emphasized O
. O

In O
Southern O
Africa O
it O
has O
been O
demonstrated O
that O
the O
small B-climate-assets
- I-climate-assets
scale I-climate-assets
agriculture I-climate-assets
as O
practiced O
in O
many O
parts O
is O
dynamic O
; O
the O
small O
- O
scale O
producers O
often O
come O
up O
with O
innovations O
that O
are O
the O
result O
of O
very O
complex O
long O
- O
term O
processes O
and O
networks O
without O
external O
interventions O
. O

The O
Response B-climate-organizations
Farming I-climate-organizations
Project I-climate-organizations
initiated O
to O
fortify O
the O
small O
- O
scale O
producers O
’ O
food B-climate-assets
security I-climate-assets
by O
helping O
them O
make O
optimal O
crop B-climate-mitigations
planning I-climate-mitigations
decisions O
through O
adapting O
their O
day O
- O
to O
- O
day O
management O
by O
responding O
to O
anticipated O
immediate O
on O
- O
hand O
crop B-climate-mitigations
- I-climate-mitigations
plant I-climate-mitigations
- I-climate-mitigations
weather I-climate-mitigations
situation O
and O
to O
the O
medium O
term O
forecasts O
for O
the O
coming O
weeks O
attest O
to O
the O
ability O
of O
small O
- O
scale O
producers O
to O
respond O
and O
adapt O
to O
weather O
and O
climate O
variability O
. O

-DOCSTART- -X- O O b194578e39e3fa77c3d1e3cedfa8223f

evaluated O
using O
high O
- O
resolution O
airborne O
light B-climate-observations
detection I-climate-observations
and I-climate-observations
ranging I-climate-observations
( O
lidar B-climate-observations
) O
snow B-climate-properties
depth I-climate-properties
data O

-DOCSTART- -X- O O f9dc66f31a9d491744e48461cfbbb22f

Arctic O
environments O
are O
generally O
believed O
to O
be O
highly O
sensitive O
to O
human O
- O
induced O
climatic O
change O
. O

In O
this O
paper O
, O
we O
explore O
the O
impacts O
on O
the O
hydrological O
system O
of O
the O
sub O
- O
arctic O
Tana O
Basin O
in O
Northernmost O
Finland O
and O
Norway O
. O

In O
contrast O
with O
previous O
studies O
, O
attention O
is O
not O
only O
given O
to O
river B-climate-nature
discharge B-climate-properties
, O
but O
also O
to O
the O
spatial O
patterns O
in O
snow B-climate-nature
coverage O
and O
evapotranspiration B-climate-properties
. O

We O
used O
a O
distributed O
water B-climate-nature
balance I-climate-nature
model O
that O
was O
coupled O
to O
a O
regional O
climate O
model O
in O
order O
to O
calculate O
a O
scenario O
of O
climate O
change O
by O
the O
end O
of O
this O
century O
. O

Three O
different O
model O
experiments O
were O
performed O
, O
adopting O
different O
approaches O
to O
using O
the O
climate O
model O
output O
in O
the O
hydrological B-climate-nature
model O
runs O
. O

-DOCSTART- -X- O O 390030e549d721470d58ccfb61dc2d8f

As O
a O
result O
of O
climate O
change O
, O
many O
lands O
are O
under O
risk O
due O
to O
the O
rising B-climate-hazards
sea I-climate-hazards
levels I-climate-hazards
( O
RSL B-climate-hazards
) O
. O

Lower O
- O
lying O
islands B-climate-nature
are O
more O
endangered O
from O
RSL B-climate-hazards
. O

One O
of O
such O
islands B-climate-nature
is O
Failaka O
, O
a O
small O
island B-climate-nature
in O
Kuwait O
lying O
at O
the O
entrance O
of O
Kuwait O
Bay O
, O
which O
is O
located O
on O
the O
north O
- O
western O
side O
of O
the O
Arabian O
Gulf O
( O
Also O
called O
the O
Persian O
Gulf O
) O
. O

Most O
of O
Failaka O
Island O
is O
lower O
than O
three O
meters O
. O

To O
detect O
these O
areas O
, O
spatial O
analysis O
of O
the O
Digital O
elevation O
model O
( O
DEM O
) O
are O
used O
. O

The O
CVI B-climate-models
shows O
that O
the O
eastern O
coast O
is O
the O
most O
susceptible O
with O
regard O
to O
the O
SLR O
. O

-DOCSTART- -X- O O e316c3882cdb7b830c18c0f4b538f97f

It O
has O
determined O
urban B-climate-mitigations
form I-climate-mitigations
characteristics I-climate-mitigations
by O
either O
studying O
urban O
form O
characteristics O
separately O
or O
studying O
the O
previous O
carbon B-climate-mitigations
mitigation I-climate-mitigations
efforts O
that O
mentioned O
some O
of O
urban B-climate-mitigations
form I-climate-mitigations
characteristics I-climate-mitigations
. O

-DOCSTART- -X- O O 6ffac37a937f405842a55f375741fb8f

We O
used O
simulation O
modeling O
to O
assess O
potential O
climate O
change O
impacts O
on O
wildfire B-climate-hazards
exposure O
in O
Italy O
and O
Corsica O
( O
France O
) O
. O

Weather O
data O
were O
obtained O
from O
a O
regional O
climate O
model O
for O
the O
period O
1981 O
- O
2070 O
using O
the O
IPCC B-climate-organizations
A1B I-climate-datasets
emission B-climate-problem-origins
scenario O
. O

Wildfire B-climate-hazards
simulations O
were O
performed O
with O
the O
minimum B-climate-models
travel I-climate-models
time I-climate-models
fire I-climate-models
spread I-climate-models
algorithm O
using O
predicted O
fuel B-climate-properties
moisture I-climate-properties
, O
wind B-climate-properties
speed I-climate-properties
, O
and O
wind B-climate-properties
direction I-climate-properties
to O
simulate O
expected O
changes O
in O
weather O
for O
three O
climatic O
periods O
( O
1981 O
- O
2010 O
, O
2011 O
- O
2040 O
, O
and O
2041 O
- O
2070 O
) O
. O

Overall O
, O
the O
wildfire B-climate-hazards
simulations O
showed O
very O
slight O
changes O
in O
flame B-climate-properties
length I-climate-properties
, O
while O
other O
outputs O
such O
as O
burn B-climate-properties
probability I-climate-properties
and O
fire B-climate-properties
size I-climate-properties
increased O
significantly O
in O
the O
second O
future O
period O
( O
2041 O
- O
2070 O
) O
, O
especially O
in O
the O
southern O
portion O
of O
the O
study O
area O
. O

The O
projected O
changes O
fuel B-climate-properties
moisture I-climate-properties
could O
result O
in O
a O
lengthening O
of O
the O
fire B-climate-hazards
season O
for O
the O
entire O
study O
area O
. O

This O
work O
represents O
the O
first O
application O
in O
Europe O
of O
a O
methodology O
based O
on O
high O
resolution O
( O
250 O
m O
) O
landscape O
wildfire B-climate-hazards
modeling O
to O
assess O
potential O
impacts O
of O
climate O
changes O
on O
wildfire B-climate-hazards
exposure O
at O
a O
national O
scale O
. O

-DOCSTART- -X- O O cecbc4abcbe1395f7db7c1e15f8ba54f

Recent O
outbreaks O
of O
infectious B-climate-impacts
pathogens I-climate-impacts
such O
as O
Zika B-climate-impacts
, O
Ebola B-climate-impacts
, O
and O
COVID-19 B-climate-impacts
have O
underscored O
the O
need O
for O
the O
dependable O
availability O
of O
vaccines B-climate-mitigations
against O
emerging B-climate-impacts
infectious I-climate-impacts
diseases I-climate-impacts
( O
EIDs B-climate-impacts
) O
. O

-DOCSTART- -X- O O 41f4f67d43408eb5a6da442cce38b11f

Soil B-climate-nature
water I-climate-nature
is O
an O
important O
variable O
in O
agricultural B-climate-assets
environments O
as O
it O
contributes O
to O
yield O
response O
as O
well O
as O
areas O
of O
environmental O
concern O
including O
erosion B-climate-hazards
, O
runoff B-climate-nature
, O
and O
nitrogen B-climate-hazards
leaching I-climate-hazards
( O
through O
deep O
drainage O
) O
. O

Crop B-climate-assets
models O
have O
been O
established O
as O
a O
method O
for O
simulating O
agricultural B-climate-assets
production O
and O
examining O
ecosystem O
responses O
. O

The O
objectives O
of O
our O
experiments O
are O
to O
( O
1 O
) O
evaluate O
the O
efficacy O
and O
feasibility O
of O
implementing O
a O
simple O
data O
assimilation O
algorithm O
for O
near B-climate-nature
- I-climate-nature
surface I-climate-nature
soil B-climate-properties
moisture I-climate-properties
in O
the O
DSSAT B-climate-models
( O
Decision B-climate-models
Support I-climate-models
System I-climate-models
for I-climate-models
Agrotechnology I-climate-models
Transfer I-climate-models
) O
Model O
and O
( O
2 O
) O
examine O
changes O
in O
yield O
from O
different O
data O
assimilation O
cases O
. O

In O
this O
paper O
we O
use O
direct O
insertion O
, O
a O
simple O
data O
assimilation O
method O
, O
to O
examine O
how O
assimilation O
of O
near B-climate-nature
- I-climate-nature
surface I-climate-nature
( O
0 O
– O
5 O
cm O
) O
soil B-climate-properties
water I-climate-properties
content I-climate-properties
observations O
impacts O
maize B-climate-assets
yields O
. O

The O
CERES B-climate-models
- I-climate-models
Maize I-climate-models
component O
of O
the O
DSSAT B-climate-models
Model O
was O
used O
for O
simulations O
. O

-DOCSTART- -X- O O dd8e6468f601c6b06915ff9acbf024ff

We O
provide O
evidence O
that O
lower B-climate-mitigations
fertility I-climate-mitigations
can O
simultaneously O
increase O
income B-climate-assets
per O
capita O
and O
lower O
carbon O
emissions B-climate-problem-origins
, O
eliminating O
a O
trade O
- O
off O
central O
to O
most O
policies O
aimed O
at O
slowing O
global O
climate O
change O
. O

First O
, O
we O
estimate O
a O
version O
of O
the O
STIRPAT B-climate-models
equation O
on O
an O
unbalanced O
yearly O
panel O
of O
cross O
- O
country O
data O
from O
1950 O
- O
2010 O
. O

Thus O
, O
regression O
results O
imply O
that O
1 O
% O
slower O
population B-climate-problem-origins
growth I-climate-problem-origins
could O
be O
accompanied O
by O
an O
increase B-climate-assets
in I-climate-assets
income I-climate-assets
per O
capita O
of O
nearly O
7 O
% O
while O
still O
lowering O
carbon O
emissions B-climate-problem-origins
. O

-DOCSTART- -X- O O 25aa979f33066df9e2691a9e47869e37

Censuses O
used O
identified O
individuals O
( O
n O
= O
3780 O
cirio B-climate-organisms
and O
2246 O
cardon B-climate-organisms
) O
and O
were O
based O
on O
repeat O
photography O
. O

-DOCSTART- -X- O O e3a397931fd676bda3cc82dab604e5f7

Clouds B-climate-nature
play O
a O
major O
role O
in O
the O
radiation B-climate-nature
budget I-climate-nature
of O
the O
earth O
- O
atmosphere O
system O
. O

They O
contribute O
to O
a O
high O
amplitude O
of O
variation O
on O
the O
time O
scale O
of O
one O
day O
. O

Current O
cloud B-climate-nature
parameterization O
schemes O
have O
significant O
deficiency O
to O
predict O
the O
diurnal O
cycle O
of O
cloud B-climate-properties
cover I-climate-properties
a O
few O
days O
in O
advance O
. O

We O
used O
four O
versions O
of O
the O
Florida B-climate-models
State I-climate-models
University I-climate-models
( I-climate-models
FSU I-climate-models
) I-climate-models
global I-climate-models
spectral I-climate-models
model I-climate-models
( O
GSM B-climate-models
) O
including O
four O
different O
cloud B-climate-nature
parameterization O
schemes O
in O
order O
to O
construct O
ensemble O
/ O
superensemble O
forecasts O
of O
cloud B-climate-properties
covers I-climate-properties
. O

Further O
, O
a O
unified O
cloud B-climate-nature
parameterization O
scheme O
is O
developed O
for O
climate O
models O
, O
which O
, O
when O
implemented O
in O
the O
FSU B-climate-models
GSM I-climate-models
, O
carries O
a O
higher O
skill O
compared O
to O
those O
of O
the O
individual O
cloud B-climate-nature
schemes O
. O

-DOCSTART- -X- O O 30cc02745a68b9c63e26d60a3a5b9cb7

Current O
status O
of O
emission B-climate-problem-origins
inventories O
in O
five O
South O
American O
countries O
( O
Argentina O
, O
Brazil O
, O
Chile O
, O
Colombia O
and O
Peru O
) O
was O
presented O
and O
discussed O
. O

National O
emission B-climate-problem-origins
inventories O
in O
South O
America O
are O
prepared O
as O
part O
of O
the O
obligations O
of O
these O
countries O
to O
the O
United O
Nations O
Framework O
Convention O
on O
Climate O
Change O
within O
the O
framework O
of O
their O
national O
communications O
. O

A O
network O
was O
established O
between O
members O
of O
the O
LA B-climate-organizations
Emissions I-climate-organizations
Inventory I-climate-organizations
Group I-climate-organizations
( O
LAEIG B-climate-organizations
) O
from O
five O
countries O
( O
Argentina O
, O
Brazil O
, O
Chile O
, O
Colombia O
and O
Peru O
) O
and O
international O
researchers O
with O
the O
aim O
to O
build O
a O
consistent O
and O
shared O
emission B-climate-problem-origins
inventory O
in O
the O
near O
future O
for O
these O
five O
countries O
. O

-DOCSTART- -X- O O 89bb73455b6cf53ad527b2f6a0b6da17

To O
provide O
a O
full O
assessment O
, O
simulations O
with O
the O
global O
chemical O
transport O
model O
GEOS B-climate-models
- I-climate-models
CHEM I-climate-models
driven O
by O
the O
NASA B-climate-models
Goddard I-climate-models
Institute I-climate-models
for I-climate-models
Space I-climate-models
Studies I-climate-models
general I-climate-models
circulation I-climate-models
model I-climate-models
( O
NASA B-climate-models
/ I-climate-models
GISS I-climate-models
GCM I-climate-models
) O
are O
conducted O
. O

To O
isolate O
the O
effects O
from O
changes O
in O
climate O
and O
anthropogenic O
emissions B-climate-problem-origins
four O
types O
of O
simulations O
are O
performed O
: O
( O
1 O
) O
present O
- O
day O
climate O
and O
emissions B-climate-problem-origins
( O
2 O
) O
future O
climate O
following O
the O
IPCC B-climate-organizations
Special B-climate-datasets
Report I-climate-datasets
on I-climate-datasets
Emission I-climate-datasets
Scenarios I-climate-datasets
( O
SRES B-climate-datasets
) O
A1B B-climate-datasets
scenario O
and O
present O
- O
day O
anthropogenic O
emissions B-climate-problem-origins
of O
ozone O
precursors O
( O
3 O
) O
present O
- O
day O
climate O
and O
future O
emissions B-climate-problem-origins
and O
( O
4 O
) O
future O
climate O
and O
future O
emissions B-climate-problem-origins
. O

Results O
indicate O
that O
climate O
change O
impact O
on O
its O
own O
leads O
to O
an O
increase O
of O
less O
than O
3 O
ppb O
in O
western O
and O
central O
Europe O
whereas O
decreases O
are O
evident O
for O
the O
rest O
of O
the O
areas O
with O
the O
highest O
( O
about O
2.5 O
ppb O
) O
in O
southeastern O
Europe O
( O
Italy O
, O
Greece O
) O
. O

Increases O
are O
attributed O
to O
the O
increases O
of O
isoprene O
biogenic B-climate-problem-origins
emissions I-climate-problem-origins
due O
to O
increasing O
temperatures O
whereas O
decreases O
are O
associated O
with O
the O
increase O
of O
water B-climate-nature
vapor I-climate-nature
over O
sea O
which O
tends O
to O
decrease O
the O
lifetime B-climate-properties
of O
ozone O
as O
well O
as O
the O
increased O
wind B-climate-properties
speeds I-climate-properties
in O
the O
2050 O
climate O
. O

-DOCSTART- -X- O O c2dc647ef16a372b4d395cc1f105517f

The O
State O
of O
Texas O
has O
historically O
faced O
hurricane B-climate-hazards
- O
related O
damage B-climate-impacts
episodes O
, O
with O
Ike O
being O
the O
most O
recent O
example O
. O

It O
is O
expected O
that O
, O
in O
the O
future O
, O
hurricanes B-climate-hazards
will O
intensify O
due O
to O
climate O
change O
causing O
greater O
surges B-climate-hazards
, O
while O
the O
attenuating O
effect O
of O
wetlands B-climate-nature
on O
storm B-climate-hazards
surges I-climate-hazards
will O
also O
be O
modified O
due O
to O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
changes O
in O
wetland B-climate-nature
vegetation I-climate-nature
type O
and O
spatial O
location O
. O

A O
coupled O
hydrodynamic O
model O
( O
ADCIRC B-climate-models
) O
and O
wave B-climate-nature
model O
( O
SWAN B-climate-models
) O
was O
employed O
, O
and O
wetlands B-climate-nature
were O
characterized O
using O
Manning B-climate-properties
's I-climate-properties
n I-climate-properties
, O
surface B-climate-properties
canopy I-climate-properties
, O
and O
surface B-climate-properties
roughness I-climate-properties
. O

The O
wetlands B-climate-nature
parameters O
were O
developed O
from O
the O
National B-climate-datasets
Land I-climate-datasets
Cover I-climate-datasets
Dataset I-climate-datasets
( O
NLCD B-climate-datasets
) O
1992 O
and O
2001 O
. O

The O
calibrated O
coupled O
model O
for O
the O
historical O
hurricane O
Bret O
was O
used O
to O
simulate O
the O
storm B-climate-hazards
surge I-climate-hazards
for O
each O
scenario O
. O

The O
results O
for O
the O
sensitivity O
analyses O
comparing O
the O
scenarios O
with O
parameters O
developed O
from O
NLCD B-climate-datasets
datasets O
with O
four O
hypothetical O
scenarios O
considering O
very O
high O
and O
low O
Manning B-climate-properties
's I-climate-properties
n I-climate-properties
and O
wind B-climate-properties
stress I-climate-properties
( O
surface B-climate-properties
canopy I-climate-properties
) O
values O
showed O
that O
, O
for O
areas O
inside O
Nueces O
Bay O
, O
the O
storm B-climate-hazards
surge I-climate-hazards
high O
could O
vary O
up O
to O
four O
times O
depending O
on O
the O
parameter O
selection O
, O
for O
areas O
inside O
Corpus O
Christi O
Bay O
, O
the O
storm B-climate-hazards
surge I-climate-hazards
high O
varied O
around O
three O
times O
and O
behind O
the O
barrier B-climate-mitigations
island I-climate-mitigations
the O
storm B-climate-hazards
surge I-climate-hazards
high O
variation O
was O
less O
than O
three O
times O
. O

This O
study O
is O
a O
first O
step O
for O
an O
evaluation O
of O
the O
impact O
that O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
, O
climate B-climate-impacts
changed I-climate-impacts
wetlands I-climate-impacts
, O
wetlands B-climate-mitigations
restoration I-climate-mitigations
, O
land B-climate-problem-origins
use I-climate-problem-origins
change I-climate-problem-origins
, O
and O
wetlands B-climate-impacts
degradation I-climate-impacts
have O
on O
hurricane B-climate-hazards
related O
surge B-climate-properties
elevation I-climate-properties
and O
extent O
in O
the O
city O
of O
Corpus O
Christi O
. O

-DOCSTART- -X- O O 7a3648d8b629f986ce4f40bda773aa2f

Climate O
change O
is O
increasingly O
being O
implicated O
in O
species B-climate-impacts
' I-climate-impacts
range I-climate-impacts
shifts I-climate-impacts
throughout O
the O
world O
, O
including O
those O
of O
important O
vector B-climate-hazards
and I-climate-hazards
reservoir I-climate-hazards
species I-climate-hazards
for O
infectious B-climate-impacts
diseases I-climate-impacts
. O

In O
North O
America O
( O
México O
, O
United O
States O
, O
and O
Canada O
) O
, O
leishmaniasis B-climate-hazards
is O
a O
vector B-climate-impacts
- I-climate-impacts
borne I-climate-impacts
disease I-climate-impacts
that O
is O
autochthonous O
in O
México O
and O
Texas O
and O
has O
begun O
to O
expand O
its O
range O
northward O
. O

-DOCSTART- -X- O O af46b1f3d5a9b01d71d89c06727906af

Output O
of O
the O
Hadley B-climate-models
Centre I-climate-models
Global I-climate-models
Climate I-climate-models
Model I-climate-models
( O
HadCM3 B-climate-models
) O
was O
used O
to O
provide O
a O
future O
climate O
scenario O
for O
precipitation B-climate-nature
in O
selected O
western O
stations O
of O
Iran O
, O
including O
Ilam O
, O
Hamedan O
, O
Kermanshah O
, O
KhoramAbad O
, O
Sanandaj O
, O
Zanjan O
. O

Because O
of O
the O
coarse O
resolution O
of O
GCM O
output O
model O
, O
a O
statistical O
downscaling O
method O
, O
LARS_WG B-climate-models
, O
was O
applied O
in O
order O
to O
obtain O
site O
specific O
daily O
weather O
Series O
. O

The O
performance O
of O
LARS B-climate-models
- I-climate-models
WG I-climate-models
during O
the O
validation O
period O
was O
suitable O
to O
reproduce O
daily O
precipitation B-climate-nature
series O
, O
therefore O
this O
model O
was O
used O
to O
provide O
future O
scenario O
of O
daily O
precipitation B-climate-nature
for O
2011 O
- O
2030 O
period O
. O

The O
simulation O
was O
forced O
by O
the O
A1B B-climate-datasets
, O
B2 B-climate-datasets
and O
A2 B-climate-datasets
emission B-climate-problem-origins
scenario O
for O
HadCM3 B-climate-models
. O

The O
most O
percentage O
change O
is O
obtained O
by O
28 O
% O
in O
the O
Kermanshah O
station O
Under O
B1 B-climate-datasets
emission B-climate-problem-origins
scenario O
during O
near O
future O
, O
but O
the O
increase O
is O
not O
significant O
. O

SPI B-climate-properties
was O
calculated O
for O
long B-climate-hazards
- I-climate-hazards
time I-climate-hazards
drought I-climate-hazards
( O
12 O
months O
) O
and O
then O
2 O
, O
5 O
, O
10 O
, O
20 O
, O
25 O
, O
50 O
, O
100 O
return O
periods O
were O
estimated O
for O
all O
stations O
. O

-DOCSTART- -X- O O  >>> bpf CHECK END IN TEST FILE

-DOCSTART- -X- O O  >>> bpf From here on original training data

-DOCSTART- -X- O Oc48a50db77cab84cb87a3baf71306771

It O
occurs O
mainly O
due O
to O
extreme B-climate-hazards
weather I-climate-hazards
conditions O
( O
e.g. O
heavy B-climate-hazards
rainfall I-climate-hazards
and O
snowmelt B-climate-properties
) O
and O
the O
consequences O
of O
flood B-climate-hazards
events O
can O
be O
devastating O
. O

For O
a O
sound O
flood B-climate-mitigations
risk I-climate-mitigations
management I-climate-mitigations
and O
mitigation B-climate-mitigations
, O
a O
proper O
risk O
assessment O
is O
needed O
. O

Anthropogenic O
climate O
change O
causes O
higher O
intensity B-climate-properties
of I-climate-properties
rainfall I-climate-properties
and O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
and O
therefore O
an O
increase O
in O
scale O
and O
frequency B-climate-properties
of O
the O
flood B-climate-hazards
events O
. O

The O
impacts O
of O
changes O
in O
risk O
components O
are O
explored O
by O
plausible O
change O
scenarios O
for O
the O
mesoscale O
Mulde O
catchment O
( O
sub B-climate-nature
- I-climate-nature
basin I-climate-nature
of O
the O
Elbe O
) O
in O
Germany O
. O

-DOCSTART- -X- O Ob1987742e27afd45a1be182176d046b9

Mandatory O
water B-climate-mitigations
use I-climate-mitigations
restrictions I-climate-mitigations
have O
become O
a O
common O
feature O
of O
the O
urban B-climate-mitigations
water I-climate-mitigations
management I-climate-mitigations
landscape O
in O
countries O
like O
Australia O
. O

Most O
interest O
in O
these O
studies O
emerged O
in O
times O
of O
drought B-climate-hazards
, O
when O
the O
severity O
of O
restrictions O
and O
their O
deployment O
had O
increased O
and O
water B-climate-mitigations
managers I-climate-mitigations
contemplate O
supply O
augmentation O
measures O
. O

A O
question O
thus O
arises O
as O
to O
whether O
the O
same O
estimates O
can O
be O
legitimately O
deployed O
to O
water B-climate-assets
supply I-climate-assets
projects O
undertaken O
when O
water O
is O
more O
plentiful O
. O

We O
report O
the O
results O
of O
a O
comparison O
between O
two O
surveys O
, O
undertaken O
in O
2008 O
and O
2012 O
, O
using O
a O
common O
multiple O
- O
bounded O
discrete O
choice O
contingent O
valuation O
design O
, O
administered O
across O
six O
cities O
in O
Australia O
, O
covering O
metropolitan O
and O
regional O
settings O
. O

We O
find O
that O
willingness O
to O
pay O
estimates O
significantly O
change O
over O
time O
in O
most O
regional O
centres O
but O
this O
is O
not O
the O
case O
for O
the O
major O
cities O
of O
Sydney O
and O
Melbourne O
, O
once O
changes O
in O
housing O
prices O
are O
included O
in O
the O
analysis O
. O

-DOCSTART- -X- O O9102a34e44a0a10e36390458280018a8

In O
northern O
countries O
, O
such O
as O
Finland O
, O
winter O
climate O
conditions O
affect O
the O
functionality O
of O
society O
in O
many O
ways O
. O

Changes O
in O
snow B-climate-nature
and O
ice B-climate-nature
act O
as O
an O
indicator O
of O
the O
climate O
conditions O
in O
a O
region O
. O

The O
main O
results O
of O
this O
work O
are O
based O
on O
gridded O
observations O
, O
FMIClimGrid B-climate-datasets
and O
E B-climate-datasets
- I-climate-datasets
OBS I-climate-datasets
, O
and O
CMIP5 B-climate-models
global O
climate O
model O
simulations O
. O

Using O
these O
, O
the O
observed O
snow B-climate-nature
, O
temperature B-climate-properties
and O
precipitation B-climate-nature
conditions O
in O
1961 O
- O
2014 O
were O
analyzed O
, O
and O
the O
future O
changes O
in O
Baltic O
Sea O
ice B-climate-nature
cover I-climate-nature
were O
projected O
for O
the O
ongoing O
century O
. O

In O
addition O
, O
two O
modeling O
studies O
were O
performed O
: O
The O
first O
assessed O
the O
performance O
of O
ECHAM5 B-climate-models
atmospheric O
general O
circulation O
model O
in O
simulating O
snow B-climate-properties
melt I-climate-properties
timing I-climate-properties
in O
spring O
, O
and O
the O
second O
studied O
the O
ability O
of O
numerical O
convection O
- O
permitting O
weather O
prediction O
model O
HARMONIE B-climate-models
to O
simulate O
a O
sea O
- O
effect O
snowfall B-climate-nature
case O
. O

The O
annual O
maximum O
sea B-climate-properties
ice I-climate-properties
extent I-climate-properties
and O
sea B-climate-properties
ice I-climate-properties
thickness I-climate-properties
in O
the O
Baltic O
Sea O
were O
projected O
to O
decrease O
during O
the O
ongoing O
century O
. O

-DOCSTART- -X- O Odd40a5ee7ba9129ca55c064267227098

ABSTRACT O
For O
Europe O
to O
meet O
its O
climate O
targets O
, O
large O
financial O
investments O
in O
the O
energy B-climate-problem-origins
sector I-climate-problem-origins
are O
required O
. O

Cost O
reductions O
for O
low B-climate-mitigations
- I-climate-mitigations
carbon I-climate-mitigations
power I-climate-mitigations
generation I-climate-mitigations
are O
critical O
to O
achieve O
these O
targets O
. O

The O
cost O
of O
capital O
has O
a O
bigger O
impact O
on O
the O
LCOE B-climate-properties
for O
renewable B-climate-mitigations
energy I-climate-mitigations
than O
for O
fossil B-climate-problem-origins
fuel I-climate-problem-origins
- O
based O
power O
production O
. O

Using O
the O
integrated O
assessment O
model O
TIAM B-climate-models
- I-climate-models
ECN I-climate-models
we O
demonstrate O
that O
Europe O
’s O
future O
energy O
system O
is O
highly O
sensitive O
to O
the O
level O
of O
financing O
costs O
. O

-DOCSTART- -X- O O7c508bd7f9078bd70c6c5c856ed822b0

Alterations O
to O
indoor O
and O
outdoor O
environments O
resulting O
from O
urbanization B-climate-problem-origins
, O
industrialization B-climate-problem-origins
, O
and O
climate O
change O
have O
significant O
implications O
for O
the O
prevalence O
and O
management O
of O
allergic B-climate-impacts
rhino I-climate-impacts
- I-climate-impacts
conjunctivitis I-climate-impacts
. O

Rising O
temperatures B-climate-properties
, O
precipitation B-climate-nature
and O
more O
extreme B-climate-hazards
weather I-climate-hazards
enable O
longer B-climate-hazards
pollen I-climate-hazards
seasons I-climate-hazards
and O
greater O
viability O
of O
indoor O
and O
outdoor O
moulds B-climate-hazards
and O
result O
in O
increased O
exposure O
to O
( O
and O
allergenic O
potential O
of O
) O
these O
aeroallergens B-climate-hazards
. O

Outdoor O
air B-climate-hazards
pollution I-climate-hazards
is O
a O
major O
risk O
factor O
for O
rhino B-climate-impacts
- I-climate-impacts
conjunctivitis I-climate-impacts
; O
key O
contributors O
are O
fuel B-climate-problem-origins
combustion I-climate-problem-origins
and O
dust B-climate-nature
storms I-climate-nature
because O
of O
changes B-climate-problem-origins
in I-climate-problem-origins
land I-climate-problem-origins
- I-climate-problem-origins
use I-climate-problem-origins
and O
development O
. O

-DOCSTART- -X- O Oac1ffa7abfe4031897d0c2a1e59dd74c

Objectives O
Many O
studies O
have O
explored O
the O
relationship O
between O
short O
- O
term O
weather O
and O
its O
health B-climate-impacts
effects I-climate-impacts
( O
including O
pneumonia B-climate-impacts
) O
based O
on O
mortality B-climate-impacts
, O
although O
both O
morbidity B-climate-impacts
and O
mortality B-climate-impacts
pose O
a O
substantial O
burden O
. O

Methods O
Daily O
records O
of O
ER O
visits O
for O
pneumonia B-climate-impacts
over O
a O
6 O
- O
year O
period O
( O
2009 O
- O
2014 O
) O
were O
collected O
from O
the O
National B-climate-datasets
Emergency I-climate-datasets
Department I-climate-datasets
Information I-climate-datasets
System I-climate-datasets
. O

Corresponding O
meteorological O
data O
were O
obtained O
from O
the O
National B-climate-datasets
Climate I-climate-datasets
Data I-climate-datasets
Service I-climate-datasets
System I-climate-datasets
. O

-DOCSTART- -X- O O42b2418b7f5e051209282f847144a831

The O
experimental O
protocol O
consists O
of O
first O
establishing O
a O
virtual O
- O
reality O
reference O
climate O
over O
a O
fairly O
large O
area O
by O
using O
the O
Canadian B-climate-observations
RCM I-climate-observations
with O
grid O
spacing O
of O
45 O
km O
nested O
within O
NCEP B-climate-organizations
analyses O
. O

The O
results O
show O
that O
complex O
topography B-climate-nature
and O
coastline B-climate-nature
have O
a O
strong O
positive O
impact O
on O
the O
downscaling O
ability O
of O
the O
one O
- O
way O
nesting O
technique O
. O

These O
surface O
forcings O
, O
found O
to O
be O
responsible O
for O
a O
large O
part O
of O
small O
- O
scale O
climate O
features O
, O
act O
primarily O
locally O
and O
yield O
good O
climate O
reproducibility O
. O

Precipitation B-climate-nature
over O
the O
Rocky O
Mountains O
region O
is O
a O
field O
in O
which O
such O
effect O
is O
found O
and O
for O
which O
the O
nesting O
technique O
displays O
significant O
downscaling O
ability O
. O

-DOCSTART- -X- O O9724d7676a60207553bfd0bccff07685

Also O
, O
it O
is O
found O
that O
an O
update B-climate-properties
frequency I-climate-properties
of O
four O
times O
a O
day O
leads O
to O
a O
better O
downscaling O
than O
twice O
a O
day O
when O
a O
ratio O
of O
spatial O
resolution O
of O
one O
is O
used O
. O

-DOCSTART- -X- O O69156a2cec8df02d805526466b74e8f2

As O
an O
indicator O
and O
regulator O
of O
climate O
and O
environmental O
change O
, O
the O
Tibet O
Plateau O
is O
an O
important O
barrier O
for O
ecological O
security O
. O

However O
, O
despite O
the O
importance O
of O
soil B-climate-organisms
microbial I-climate-organisms
communities I-climate-organisms
in O
almost O
all O
soil O
biochemical O
processes O
and O
ecosystem O
functions O
, O
the O
biogeography O
of O
soil B-climate-organisms
microbial I-climate-organisms
communities I-climate-organisms
on O
the O
Tibet O
Plateau O
is O
poorly O
understood O
, O
especially O
at O
large O
scales O
over O
different O
ecosystem O
types O
. O

We O
then O
used O
next O
generation O
high O
- O
throughput O
sequencing O
to O
investigate O
the O
soil O
prokaryote B-climate-organisms
community O
( O
i.e. O
bacteria B-climate-organisms
and O
archaea B-climate-organisms
) O
diversity B-climate-organisms
and O
spatial O
patterns O
and O
to O
explore O
their O
relationship O
with O
biotic O
( O
e.g. O
plant B-climate-organisms
functional O
group O
diversity B-climate-organisms
and O
biomass O
) O
and O
abiotic O
( O
e.g. O
aridity B-climate-properties
index I-climate-properties
, O
soil O
carbon O
and O
nitrogen O
levels O
) O
factors O
. O

Among O
the O
four O
alpine B-climate-nature
grassland I-climate-nature
types O
( O
i.e. O
alpine B-climate-nature
meadow I-climate-nature
, O
alpine B-climate-nature
steppe I-climate-nature
, O
alpine B-climate-nature
shrub I-climate-nature
and O
alpine B-climate-nature
desert I-climate-nature
) O
sampled O
in O
this O
study O
, O
alpine B-climate-nature
meadow I-climate-nature
had O
the O
highest O
soil B-climate-organisms
microbial I-climate-organisms
biomass O
and O
alpine B-climate-nature
desert I-climate-nature
had O
the O
lowest O
soil B-climate-organisms
microbial I-climate-organisms
richness O
and O
Shannon O
diversity O
. O

-DOCSTART- -X- O O0de36c3a42e93c73ddfe93e156bc40b3

The O
Great B-climate-organizations
Lakes I-climate-organizations
Environmental I-climate-organizations
Research I-climate-organizations
Laboratory I-climate-organizations
has O
developed O
conceptual O
models O
for O
simulating O
moisture B-climate-nature
storages I-climate-nature
in O
and O
runoff B-climate-nature
from O
the O
121 O
watersheds B-climate-nature
draining O
into O
the O
Laurentian O
Great O
Lakes O
, O
overlake O
precipitation B-climate-nature
into O
each O
lake B-climate-nature
, O
the O
heat B-climate-nature
storages I-climate-nature
in O
and O
evaporation B-climate-nature
from O
each O
lake B-climate-nature
, O
connecting O
channel B-climate-nature
flows I-climate-nature
and O
lake B-climate-nature
levels O
, O
and O
regulation O
of O
flows O
at O
control O
points O
. O

We O
determine O
net B-climate-properties
water I-climate-properties
supplies I-climate-properties
and I-climate-properties
levels I-climate-properties
for O
each O
lake B-climate-nature
to O
consider O
climate O
change O
scenarios O
developed O
from O
atmospheric O
general O
circulation O
models O
through O
linkages O
on O
air B-climate-properties
temperature I-climate-properties
, O
precipitation B-climate-nature
, O
humidity B-climate-properties
, O
wind B-climate-properties
speed I-climate-properties
, O
and O
cloud B-climate-properties
cover I-climate-properties
. O

Scenarios O
of O
a O
doubling O
of O
atmospheric O
CO B-climate-greenhouse-gases
2 I-climate-greenhouse-gases
are O
considered O
by O
abstracting O
changes O
in O
linkages O
, O
making O
these O
changes O
in O
historical O
data O
, O
observing O
the O
impact O
of O
the O
changed O
data O
in O
model O
outputs O
, O
and O
comparing O
it O
to O
model O
results O
obtained O
from O
unchanged O
data O
. O

-DOCSTART- -X- O O11b2c16f79f83d4e807cc9db4e6b44a0

As O
an O
important O
policy O
instrument O
for O
climate O
mitigation B-climate-mitigations
, O
the O
carbon B-climate-mitigations
tax I-climate-mitigations
policy O
design O
and O
its O
consequent O
social O
- O
economic O
impact O
calls O
for O
more O
research O
. O

In O
this O
paper O
, O
a O
dynamic O
Computable O
General O
Equilibrium O
( O
CGE O
) O
model O
– O
CASIPM B-climate-models
- I-climate-models
GE I-climate-models
model O
is O
applied O
to O
explore O
the O
impact O
of O
a O
carbon B-climate-mitigations
tax I-climate-mitigations
and O
different O
tax B-climate-mitigations
revenue I-climate-mitigations
recycling I-climate-mitigations
schemes O
on O
China O
’s O
economy O
. O

-DOCSTART- -X- O O60b1867718d4335030262fbe97a30d30

Abstract O
We O
examined O
how O
butterfly B-climate-organisms
species O
richness O
is O
affected O
by O
human O
impact O
and O
elevation B-climate-properties
, O
and O
how O
species O
ranges O
are O
distributed O
along O
the O
elevational O
gradient O
( O
200–2700 O
m O
) O
in O
the O
Isère O
Department O
( O
French O
Alps O
) O
. O

A O
total O
of O
35,724 O
butterfly B-climate-organisms
observations O
gathered O
in O
summer O
( O
May O
– O
September O
) O
between O
1995 O
and O
2015 O
were O
analyzed O
. O

Estimations O
were O
also O
performed O
on O
a O
500 O
m O
× O
500 O
m O
grid O
at O
low O
altitude B-climate-properties
( O
200–500 O
m O
) O
to O
test O
for O
the O
human O
impact O
on O
species O
richness O
using O
generalized O
least O
squares O
regression O
models O
. O

Butterfly B-climate-organisms
diversity B-climate-organisms
is O
exceptionally O
high O
( O
185 O
species O
) O
in O
this O
alpine O
department O
that O
represents O
less O
than O
5 O
% O
of O
the O
French O
territory O
and O
yet O
holds O
more O
than O
70 O
% O
of O
all O
the O
Rhopalocera B-climate-organisms
species O
recorded O
in O
France O
. O

-DOCSTART- -X- O O244eba7942630e0a0fbcbb9e31c657f3

The O
results O
also O
highlight O
the O
need O
to O
further O
develop O
the O
Aglink B-climate-models
- I-climate-models
Cosimo I-climate-models
model O
to O
include O
the O
adop O
tion O
of O
new O
technologies O
and O
to O
account O
for O
CO2 B-climate-greenhouse-gases
emissions O
and O
removals O
related O
to O
land B-climate-problem-origins
use I-climate-problem-origins
changes I-climate-problem-origins
, O
to O
get O
a O
broader O
picture O
of O
the O
possible O
contribution O
( O
and O
resulting O
impacts O
) O
of O
ag O
riculture O
to O
a O
global O
low B-climate-mitigations
carbon I-climate-mitigations
economy I-climate-mitigations
. O

-DOCSTART- -X- O O46cf7d4aee7f8bf0011c155e50c9901b

In O
this O
part O
of O
the O
Mediterranean O
, O
the O
general O
warming O
trend O
was O
strongly O
enhanced O
by O
changes O
in O
the O
atmospheric B-climate-nature
circulation I-climate-nature
patterns O
, O
characterized O
by O
a O
northward O
extension O
of O
the O
subtropical B-climate-nature
high O
pressure O
domain O
during O
spring O
and O
summer O
. O

Although O
annual O
precipitation B-climate-nature
did O
not O
follow O
clear O
trends O
, O
water O
discharge B-climate-properties
significantly O
decreased O
in O
one O
third O
of O
the O
watersheds B-climate-nature
and O
accounted O
for O
an O
estimated O
20 O
% O
reduction O
of O
the O
water B-climate-nature
resources I-climate-nature
in O
this O
region O
. O

-DOCSTART- -X- O Od5b060fcbfc6aa69e9b70021ac00e343

We O
examined O
the O
potential O
added O
risk O
posed O
by O
global O
climate O
change O
on O
the O
dengue B-climate-impacts
vector O
Aedes B-climate-hazards
aegypti I-climate-hazards
abundance O
using O
CLIMEX B-climate-models
, O
a O
powerful O
tool O
for O
exploring O
the O
relationship O
between O
the O
fundamental O
and O
realised O
niche O
of O
any O
species O
. O

The O
impact O
of O
climate O
change O
on O
its O
potential O
distribution O
was O
assessed O
with O
two O
global O
climate O
models O
, O
the O
CSIRO B-climate-models
- I-climate-models
Mk3.0 I-climate-models
and O
the O
MIROC B-climate-models
- I-climate-models
H I-climate-models
, O
run O
with O
two O
potential O
, O
future O
emission B-climate-problem-origins
scenario O
( O
A1B B-climate-datasets
and O
A2 B-climate-datasets
) O
published O
by O
the O
Intergovernmental B-climate-organizations
Panel I-climate-organizations
on I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
. O

However O
, O
even O
if O
much O
of O
the O
tropics B-climate-nature
and O
subtropics B-climate-nature
will O
continue O
to O
be O
suitable O
, O
the O
climatically O
favourable O
areas O
for O
A. B-climate-hazards
aegypti I-climate-hazards
globally O
are O
projected O
to O
contract O
under O
the O
future O
scenarios O
produced O
by O
these O
models O
, O
while O
currently O
unfavourable O
areas O
, O
such O
as O
inland O
Australia O
, O
the O
Arabian O
Peninsula O
, O
southern O
Iran O
and O
some O
parts O
of O
North O
America O
may O
become O
climatically O
favourable O
for O
this O
mosquito B-climate-hazards
species O
. O

-DOCSTART- -X- O O07541878e530a854779e772a55ccbd21

ABSTRACT O
In O
order O
to O
understand O
better O
on O
medium O
- O
and O
long O
- O
term O
climate O
change O
issues O
in O
international O
cooperation O
of O
the O
Belt B-climate-organizations
and I-climate-organizations
Road I-climate-organizations
Initiative I-climate-organizations
( O
BRI B-climate-organizations
) O
, O
this O
paper O
is O
meant O
to O
assess O
the O
implementation O
of O
National O
Determined O
Contributions O
( O
NDCs O
) O
of O
the O
BRI B-climate-organizations
countries O
and O
the O
emission O
constraints O
under O
the O
Paris B-climate-mitigations
Agreement I-climate-mitigations
to O
hold O
the O
increase O
in O
the O
global B-climate-properties
average I-climate-properties
temperature I-climate-properties
to O
well O
below O
2 O
° O
C O
above O
pre O
- O
industrial O
levels O
, O
based O
on O
the O
Belt B-climate-models
and I-climate-models
Road I-climate-models
Integrated I-climate-models
Assessment I-climate-models
Model I-climate-models
( O
BRIAM B-climate-models
) O
and O
the O
best O
available O
data O
. O

-DOCSTART- -X- O Oa4237bf52c53fddb5b3830f47ce82b3d

Data O
were O
obtained O
from O
the O
Australian B-climate-organizations
Bureau I-climate-organizations
of I-climate-organizations
Meteorology I-climate-organizations
( O
BoM B-climate-organizations
) O
from O
136 O
high O
- O
quality O
weather O
stations O
. O

To O
reduce O
spatial O
complexity O
, O
climate O
regionalization O
was O
used O
to O
divide O
the O
stations O
in O
homogenous O
sub O
- O
regions O
based O
on O
similarity O
of O
rainfall B-climate-nature
patterns O
and O
intensity B-climate-properties
using O
principal O
component O
analysis O
( O
PCA O
) O
and O
K O
- O
means O
clustering O
. O

-DOCSTART- -X- O Oa958aaec70c19a08e07c701ead762245

The O
aim O
of O
this O
thesis O
was O
to O
examine O
the O
recovery O
of O
both O
structural O
and O
functional O
attributes O
of O
ecosystem O
undergoing O
restoration B-climate-mitigations
using O
the O
Changting O
model O
restoration B-climate-mitigations
site O
as O
a O
case O
. O

Trees B-climate-organisms
and O
shrubs B-climate-organisms
were O
inventoried O
in O
field O
and O
carbon O
stock O
was O
estimated O
with O
existing O
allometric O
equations O
. O

Understory B-climate-nature
vegetation I-climate-nature
and O
forest B-climate-nature
floor I-climate-nature
detritus I-climate-nature
were O
harvested O
and O
soil B-climate-nature
samples O
collected O
in O
the O
field O
for O
measuring O
carbon O
and O
nitrogen O
content O
using O
an O
elemental O
analyzer O
. O

General O
liner O
model O
analysis O
of O
variance O
( O
ANOVA O
) O
was O
preformed O
to O
determine O
significant O
differences O
between O
sites O
. O

-DOCSTART- -X- O Ob6d0d8237090211f52beb76507ca23a2

The O
total O
soil B-climate-properties
carbon I-climate-properties
stock I-climate-properties
decreased O
in O
the O
following O
order O
: O
SF O
( O
65 O
± O
12.3 O
tC O
ha⁻¹ O
) O
, O
OS O
( O
49.9 O
± O
21.3 O
tC O
ha⁻¹ O
) O
, O
YS O
( O
35.1 O
± O
21.1 O
tC O
ha⁻¹ O
) O
and O
DS O
( O
6.6 O
± O
3 O
t O
C O
ha⁻¹ O
) O
. O

-DOCSTART- -X- O O6efe1eb0cf5148ea02b30a59b402d51b

A O
secondary O
objective O
is O
to O
improve O
the O
representation O
of O
climate O
variability O
within O
IAM O
's O
. O

-DOCSTART- -X- O Of68a4c86b58931f017dafcf37677e60a

Change O
in O
seawater B-climate-properties
pH I-climate-properties
between O
1991 O
and O
2006 O
along O
152 O
° O
W O
in O
the O
North O
Pacific O
Ocean O
. O

Accurate O
measurements O
of O
pH B-climate-properties
are O
necessary O
for O
scientists O
to O
make O
such O
time O
- O
lapse O
observations O
of O
change O
, O
especially O
in O
regions O
that O
are O
strongly O
affected O
by O
physical O
and O
biological O
conditions O
such O
as O
the O
Kuroshio B-climate-nature
Extension I-climate-nature
off O
the O
east O
coast O
of O
Japan O
. O

-DOCSTART- -X- O O7f0838f648d437442f0fbde1ea87a9c9

Stipa B-climate-organisms
tenacissima I-climate-organisms
L. I-climate-organisms
( O
Alfa B-climate-organisms
grass I-climate-organisms
) O
is O
an O
important O
perennial O
grass B-climate-organisms
species O
in O
Tunisia O
and O
Northern O
Africa O
which O
dominates O
wide O
arid B-climate-nature
ecosystems I-climate-nature
offering O
multiple O
services O
. O

To O
investigate O
the O
potential O
effects O
of O
climate O
change O
on O
the O
target O
species O
we O
used O
Maxent O
modeling O
algorithm O
for O
two O
representative O
concentration O
pathways O
( O
RCPs O
) O
lower O
emission B-climate-problem-origins
scenario O
( O
RCP B-climate-datasets
2.6 I-climate-datasets
) O
and O
higher O
emission O
( O
RCP B-climate-datasets
8.5 I-climate-datasets
) O
climate O
forcing O
scenarios O
in O
2050 O
and O
2070 O
. O

-DOCSTART- -X- O Ofadf8d7c1179335056970678cc97ebe0

Predicting O
weather O
and O
climate O
and O
its O
impacts O
on O
the O
environment O
, O
including O
hazards O
such O
as O
floods B-climate-hazards
and O
landslides B-climate-hazards
, O
is O
a O
big O
challenge O
that O
can O
be O
efficiently O
supported O
by O
a O
distributed O
and O
heterogeneous O
infrastructure B-climate-assets
, O
exploiting O
several O
kinds O
of O
computational O
resources O
: O
HPC O
, O
Grids O
and O
Clouds O
. O

-DOCSTART- -X- O O2e6a5e32557dec999b3af1e724791001

The O
Northwestern O
Hawaiian O
Islands O
( O
NWHI O
) O
have O
high O
conservation O
value O
due O
to O
their O
concentration O
of O
endemic B-climate-organisms
, O
endangered B-climate-organisms
and I-climate-organisms
threatened I-climate-organisms
spe- I-climate-organisms
cies I-climate-organisms
, O
and O
large O
numbers O
of O
nesting O
seabirds B-climate-organisms
. O

Most O
of O
these O
islands B-climate-nature
are O
low O
- O
lying O
and O
therefore O
poten- O
tially O
vulnerable O
to O
increases O
in O
global B-climate-properties
average I-climate-properties
sea I-climate-properties
level I-climate-properties
. O

Sea B-climate-properties
level I-climate-properties
is O
expected O
to O
continue O
increasing O
after O
2100 O
, O
which O
would O
have O
greater O
impact O
on O
atolls B-climate-nature
such O
as O
French O
Frigate O
Shoals O
and O
Pearl O
and O
Hermes O
Reef O
, O
where O
virtually O
all O
land O
is O
less O
than O
2 O
m O
above B-climate-properties
sea I-climate-properties
level I-climate-properties
. O

Higher O
elevation O
islands B-climate-nature
such O
as O
Lisianski O
, O
Laysan O
, O
Necker O
, O
and O
Nihoa O
may O
provide O
longer- O
term O
refuges O
for O
species B-climate-organisms
. O

The O
effects O
of O
habitat B-climate-hazards
loss I-climate-hazards
on O
NWHI O
biota B-climate-organisms
are O
difficult O
to O
predict O
, O
but O
may O
be O
greatest O
for O
endangered B-climate-hazards
Hawaiian B-climate-organisms
monk I-climate-organisms
seals I-climate-organisms
, O
threatened B-climate-hazards
Hawaiian B-climate-organisms
green I-climate-organisms
sea I-climate-organisms
turtles I-climate-organisms
, O
and O
the O
endangered B-climate-hazards
Laysan B-climate-organisms
finch I-climate-organisms
at O
Pearl O
and O
Hermes O
Reef O
. O

-DOCSTART- -X- O O29f0dca5922896cc9ec8bd8c0713ea54

simulate O
the O
climate O
- O
driven O
streamflow B-climate-properties
in O
data O
- O
scarce O
mountain B-climate-nature
basins I-climate-nature
of O
Northwest O
China O
, O
we O
developed O
an O
integrated O
approach O
by O
using O
downscaled O
reanalysis O
data O
, O
Mann O
– O
Kendall O
test O
, O
ensemble O
empirical O
mode O
decomposition O
and O
backpropagation O
artificial O
neural O
networks O
together O
with O
the O
weights O
connection O
method O
. O

We O
validated O
the O
approach O
in O
the O
Kaidu O
River O
basin O
located O
in O
the O
Tianshan O
mountains O
. O

-DOCSTART- -X- O O5d40271eb0ee584db433f4de4a8fee31

A O
conceptual O
hydrological B-climate-nature
model O
is O
calibrated O
on O
data O
from O
four O
mesoscale O
, O
mountainous B-climate-nature
catchments I-climate-nature
in O
south O
Ecuador O
. O

The O
model O
inputs O
are O
then O
perturbed O
with O
anomalies O
projected O
by O
20 O
GCMs O
available O
from O
the O
IPCC B-climate-organizations
Data I-climate-organizations
Distribution I-climate-organizations
Centre I-climate-organizations
. O

-DOCSTART- -X- O O76ad20c408787304edbe104404d48b75

A O
global B-climate-properties
warming I-climate-properties
of O
2 O
° O
C O
is O
predicted O
to O
drive O
almost O
half O
the O
world O
’s O
lizard B-climate-organisms
populations O
to O
extinction B-climate-hazards
. O

Urban B-climate-hazards
heat I-climate-hazards
island I-climate-hazards
( O
UHI B-climate-hazards
) O
effects O
may O
further O
exacerbate O
the O
impacts O
of O
climate O
change O
on O
organisms O
that O
are O
sensitive O
to O
small O
changes O
in O
temperature B-climate-properties
. O

Currently O
, O
the O
Phoenix O
metropolitan O
region O
in O
Arizona O
, O
USA O
, O
is O
an O
average O
of O
3 O
° O
C O
warmer O
than O
the O
surrounding O
desert B-climate-nature
. O

With O
continuing O
urbanization B-climate-problem-origins
and O
climate O
change O
, O
thermal B-climate-hazards
stress I-climate-hazards
will O
become O
an O
increasingly O
important O
facet O
of O
urban O
ecology O
in O
coming O
decades O
. O

The O
main O
objective O
of O
our O
study O
was O
to O
investigate O
which O
landscaping B-climate-mitigations
styles I-climate-mitigations
and O
microhabitat B-climate-organisms
variables O
can O
most O
effectively O
reduce O
the O
surface B-climate-properties
temperatures I-climate-properties
experienced O
by O
lizards B-climate-organisms
. O

Using O
a O
bare O
lot O
as O
a O
control O
, O
we O
placed O
copper O
lizard B-climate-organisms
models O
with O
data O
loggers O
in O
several O
vegetation B-climate-nature
and O
irrigation B-climate-mitigations
treatments O
that O
represent O
the O
dominant O
backyard O
landscaping B-climate-mitigations
styles I-climate-mitigations
in O
Phoenix O
( O
grassy B-climate-nature
mesic I-climate-nature
with O
mist B-climate-mitigations
irrigation I-climate-mitigations
, O
drip B-climate-mitigations
irrigated I-climate-mitigations
xeric B-climate-nature
, O
unirrigated B-climate-mitigations
native O
, O
and O
a O
hybrid O
style O
known O
as O
oasis O
) O
. O

Shade B-climate-properties
, O
humidity B-climate-properties
, O
and O
sky B-climate-properties
view I-climate-properties
factor I-climate-properties
explained O
the O
majority O
of O
variation O
in O
temperature B-climate-properties
at O
a O
sub O
- O
meter O
scale O
. O

-DOCSTART- -X- O Ob31d9dca636173d338eeb4edd2ef73d5

Meknes O
has O
an O
agrarian O
economy O
and O
wheat B-climate-assets
production O
is O
of O
paramount O
importance O
. O

Rainfall B-climate-nature
variability O
is O
assessed O
utilizing O
the O
precipitation B-climate-properties
concentration I-climate-properties
index I-climate-properties
and O
the O
variation O
coefficient O
. O

Yields O
fluctuated O
from O
210 O
to O
4500 O
Kg O
/ O
ha O
with O
52 O
% O
of O
coefficient O
of O
variation O
. O

Addressing O
this O
challenge O
involves O
the O
recovery O
and O
recycling B-climate-mitigations
of O
materials O
to O
reduce O
consumption O
of O
raw B-climate-assets
materials I-climate-assets
, O
so O
innovation O
must O
therefore O
be O
promoted O
in O
the O
prevention O
and O
management O
of O
waste B-climate-problem-origins
, O
as O
a O
strategy O
towards O
a O
sustainable O
urban O
development O
. O

Packaging B-climate-problem-origins
is O
part O
of O
our O
current O
culture O
and O
is O
related O
to O
the O
degree O
of O
development O
of O
countries O
and O
regions O
; O
the O
construction O
sector O
is O
no O
stranger O
to O
this O
problem O
and O
generates O
a O
significant O
amount O
of O
packaging B-climate-problem-origins
waste I-climate-problem-origins
in O
the O
site O
works O
, O
which O
nowadays O
is O
not O
managed O
properly O
. O

The O
current O
Construction B-climate-problem-origins
Demolition I-climate-problem-origins
Waste I-climate-problem-origins
( O
CDW B-climate-problem-origins
) O
management O
model O
is O
ineffective O
, O
since O
landfills B-climate-problem-origins
continue O
to O
receive O
large O
amounts O
of O
recoverable O
waste B-climate-problem-origins
. O

-DOCSTART- -X- O Ocdb4e05866a25ac30b9c2be466a9f96a

Storm B-climate-mitigations
water I-climate-mitigations
management I-climate-mitigations
is O
being O
developed O
to O
restore O
the O
natural O
state O
of O
water O
cycle O
undergoing O
several O
processes O
which O
were O
hindered O
such O
as O
infiltration B-climate-properties
and O
evapotranspiration B-climate-properties
. O

Low B-climate-mitigations
Impact I-climate-mitigations
Development I-climate-mitigations
( O
LID B-climate-mitigations
) O
was O
established O
in O
order O
to O
reduce O
the O
negative O
effects O
of O
urbanization B-climate-problem-origins
to O
our O
environment O
. O

These O
developments O
can O
be O
used O
to O
respond O
to O
the O
effects O
of O
climate O
change O
such O
as O
heat B-climate-hazards
island I-climate-hazards
phenomenon O
. O

This O
study O
was O
conducted O
to O
simulate O
the O
urban B-climate-nature
hydrologic I-climate-nature
cycle I-climate-nature
simulation O
on O
Asan O
- O
Tangjeong O
in O
Korea O
. O

Lastly O
, O
this O
study O
generated O
a O
model O
using O
the O
recently O
updated O
SWMM5 B-climate-models
which O
determined O
the O
hydrologic B-climate-nature
cycle I-climate-nature
simulation O
after O
installation O
of O
LID B-climate-mitigations
facilities O
. O

-DOCSTART- -X- O O979a2b11e27c396bb99582eb5c81490d

Fresh O
groundwater B-climate-nature
in O
an O
island B-climate-nature
aquifer B-climate-nature
is O
an O
extremely O
important O
resource O
that O
is O
highly O
vulnerable O
to O
variations O
in O
natural O
weather O
cycles O
and O
climate O
change O
effects O
. O

On O
small O
islands B-climate-nature
, O
precipitation B-climate-nature
creates O
subsurface B-climate-nature
freshwater I-climate-nature
lenses I-climate-nature
that O
float O
on O
top O
of O
coalesced O
saltwater B-climate-nature
that O
has O
intruded O
from O
the O
surrounding O
seawater B-climate-nature
. O

The O
model O
was O
then O
extended O
to O
simulate O
a O
freshwater B-climate-nature
lens I-climate-nature
in O
Dauphin O
Island O
, O
Alabama O
, O
to O
assess O
changes O
in O
freshwater B-climate-nature
storage O
under O
realistic O
wet O
and O
dry O
recharge O
cycles O
. O

-DOCSTART- -X- O O79f3cee0054066b2ed3df151c06235ca

This O
study O
used O
the O
APSIM B-climate-models
- I-climate-models
Wheat I-climate-models
module O
and O
information O
drawn O
from O
the O
Special B-climate-datasets
Report I-climate-datasets
on I-climate-datasets
Emission I-climate-datasets
Scenarios I-climate-datasets
( O
SRES B-climate-datasets
) O
and O
nine O
climate O
models O
for O
2080 O
. O

The O
most O
likely O
wheat B-climate-assets
yield O
changes O
have O
been O
defined O
under O
combinations O
of O
changes O
in O
regional O
rainfall B-climate-nature
, O
regional O
temperature B-climate-properties
and O
atmospheric O
CO2 B-climate-properties
concentration I-climate-properties
( O
CO2 B-climate-greenhouse-gases
) O
. O

-DOCSTART- -X- O O5467e6b792ea7b45ec84fa3d113b10a0

Here O
we O
explicitly O
couple O
bioclimatic O
envelope O
models O
of O
climate O
and O
habitat B-climate-organisms
suitability O
with O
generic O
life O
- O
history O
models O
for O
24 O
species B-climate-organisms
of O
frogs B-climate-organisms
found O
in O
the O
Australian O
Wet O
Tropics O
( O
AWT O
) O
. O

We O
show O
that O
( O
i O
) O
as O
many O
as O
four O
species B-climate-organisms
of O
frogs B-climate-organisms
face O
imminent O
extinction B-climate-hazards
by O
2080 O
, O
due O
primarily O
to O
climate O
change O
; O
( O
ii O
) O
three O
frogs B-climate-organisms
face O
delayed O
extinctions B-climate-hazards
; O
and O
( O
iii O
) O
this O
extinction B-climate-hazards
debt O
will O
take O
at O
least O
a O
century O
to O
be O
realized O
in O
full O
. O

-DOCSTART- -X- O O239cb155ce50de839fb2dc13782c611d

The O
development O
of O
beneficial O
management O
practices O
is O
a O
key O
strategy O
to O
reduce O
greenhouse O
gas O
( O
GHG O
) O
emissions O
from O
animal B-climate-problem-origins
agriculture I-climate-problem-origins
. O

The O
objective O
of O
the O
present O
study O
was O
to O
evaluate O
the O
impact O
of O
time O
and O
amount O
of O
hog B-climate-problem-origins
manure I-climate-problem-origins
application O
on O
farm O
productivity O
and O
GHG O
emissions O
from O
a O
cow B-climate-assets
– O
calf B-climate-assets
production O
system O
using O
two O
whole O
- O
farm O
models O
. O

All O
three O
treatments O
were O
simulated O
in O
a O
representative O
cow O
– O
calf O
production O
system O
at O
the O
farm O
- O
gate O
using O
the O
following O
whole O
- O
farm B-climate-assets
models O
: O
a O
Coupled O
Components O
Model O
( O
CCM O
) O
that O
used O
existing O
farm B-climate-assets
component O
models O
and O
the O
Integrated B-climate-models
Farm I-climate-models
System I-climate-models
Model I-climate-models
( O
IFSM B-climate-models
) O
. O

Annual O
GHG B-climate-properties
intensities I-climate-properties
for O
the O
baseline O
scenario O
were O
17.7 O
kg O
CO2 O
- O
eq O
/ O
kg O
liveweight B-climate-properties
for O
CCM O
and O
18.1 O
kg O
CO2 O
- O
eq O
/ O
kg O
liveweight B-climate-properties
for O
IFSM B-climate-models
. O

Of O
the O
total O
farm O
GHG O
emissions O
, O
73–77 O
% O
were O
from O
enteric B-climate-problem-origins
methane B-climate-greenhouse-gases
production O
. O

-DOCSTART- -X- O O14a24454198644e4174109f5423d3de2

Vinecology B-climate-assets
, O
the O
integration O
of O
ecological O
and O
viticultural B-climate-assets
practices O
, O
focuses O
on O
the O
working O
landscapes O
of O
the O
mediterranean B-climate-nature
- I-climate-nature
climate I-climate-nature
biomes I-climate-nature
to O
make O
wine B-climate-assets
grape B-climate-assets
production O
compatible O
with O
species B-climate-organisms
conservation I-climate-organisms
. O

We O
examined O
how O
maintaining O
remnant B-climate-nature
native I-climate-nature
vegetation I-climate-nature
and O
surrounding O
natural O
areas O
in O
and O
around O
vineyards B-climate-assets
, O
two O
primary O
practices O
of O
vinecology B-climate-assets
, O
may O
influence O
bird B-climate-organisms
community O
richness O
and O
composition O
across O
a O
vineyard B-climate-assets
landscape O
. O

We O
used O
generalized O
linear O
mixed O
models O
( O
GLMMs O
) O
to O
examine O
individual O
species B-climate-organisms
responses O
to O
remnant B-climate-nature
vegetation I-climate-nature
in O
the O
vineyard B-climate-assets
at O
plot O
scale O
( O
50 O
m O
radius O
) O
given O
the O
extent O
of O
surrounding O
natural O
area O
at O
the O
landscape O
scale O
( O
500 O
- O
1000 O
m O
annular O
) O
. O

We O
used O
Horn B-climate-properties
similarity I-climate-properties
index I-climate-properties
to O
explore O
overall O
community O
differences O
to O
quantify O
variations O
in O
endemic B-climate-organisms
species I-climate-organisms
, O
guild O
detection O
levels O
, O
and O
species O
richness O
between O
site O
types O
. O

-DOCSTART- -X- O O08e8cb3e60279d29bb344405f0d6e5c0

The O
near B-climate-properties
- I-climate-properties
surface I-climate-properties
air I-climate-properties
temperature I-climate-properties
over O
China O
is O
simulated O
from O
1950 O
to O
2099 O
using O
the O
PRECIS B-climate-models
model O
from O
the O
Met B-climate-organizations
Office I-climate-organizations
Hadley I-climate-organizations
Centre I-climate-organizations
at O
a O
25 O
- O
km O
resolution O
. O

In O
order O
to O
reflect O
the O
different O
parametric O
and O
structural O
uncertainties O
in O
future O
temperature O
projections O
, O
the O
PRECIS B-climate-models
model O
is O
driven O
by O
five O
lateral O
boundary O
conditions O
, O
which O
include O
a O
four O
- O
member O
HadCM3 B-climate-models
- O
based O
perturbed O
- O
physics O
ensemble O
( O
i.e. O
, O
HadCM3Q0 B-climate-models
, O
Q1 O
, O
Q7 O
and O
Q13 O
) O
and O
an O
ECHAM5 B-climate-models
model O
. O

-DOCSTART- -X- O O85a8d217b2c6cffc525b39ee53fde131

ABSTRACT O
Achieving O
long O
- O
term O
climate O
mitigation B-climate-mitigations
goals O
in O
Japan O
faces O
several O
challenges O
, O
starting O
with O
the O
uncertain O
nuclear B-climate-mitigations
power I-climate-mitigations
policy O
after O
the O
2011 O
earthquake B-climate-hazards
, O
the O
uncertain O
availability O
and O
progress O
of O
energy O
technologies O
, O
as O
well O
as O
energy B-climate-assets
security I-climate-assets
concerns O
in O
light O
of O
a O
high O
dependency O
on O
fuel B-climate-problem-origins
imports I-climate-problem-origins
. O

We O
applied O
a O
general O
equilibrium O
energy O
economic O
model O
to O
assess O
these O
impacts O
on O
an O
80 O
% O
emission B-climate-mitigations
reduction I-climate-mitigations
target I-climate-mitigations
by O
2050 O
considering O
several O
alternative O
scenarios O
for O
nuclear B-climate-mitigations
power I-climate-mitigations
deployment O
, O
technology O
availability O
, O
end B-climate-mitigations
use I-climate-mitigations
energy I-climate-mitigations
efficiency I-climate-mitigations
, O
and O
the O
price O
of O
fossil B-climate-problem-origins
fuels I-climate-problem-origins
. O

The O
economic O
impacts O
of O
limiting O
nuclear B-climate-mitigations
power I-climate-mitigations
by O
2050 O
( O
3.5 O
% O
GDP B-climate-properties
loss I-climate-properties
) O
were O
small O
compared O
to O
the O
lack O
of O
carbon O
capture O
and O
storage O
( O
CCS O
) O
( O
6.4 O
% O
GDP B-climate-properties
loss I-climate-properties
) O
. O

-DOCSTART- -X- O O05fd637e639f3f8eb8635d1cd78ca1b2

The O
key O
findings O
of O
the O
Qilian O
Mountains O
Scientific O
Expedition O
were O
as O
follows O
: O

( O
1 O
) O
The O
ecological O
service O
functions O
of O
the O
Qilian O
Mountains O
are O
promoted O
by O
tightening O
eco B-climate-mitigations
- I-climate-mitigations
environmental I-climate-mitigations
management I-climate-mitigations
. O

( O
2 O
) O
the O
ecosystem O
of O
the O
Qilian O
Mountains O
is O
recovering O
under O
a O
warm O
and O
wet O
climate O
, O
and O
the O
population O
of O
rare B-climate-organisms
species I-climate-organisms
is O
expanding O
; O
but O
localized O
overgrazing B-climate-problem-origins
is O
causing O
grassland B-climate-nature
degradation B-climate-impacts
in O
some O
areas O
. O

( O
3 O
) O
the O
glacier B-climate-nature
loss B-climate-properties
rate I-climate-properties
is O
accelerating O
, O
and O
the O
contribution O
of O
glacial B-climate-nature
melt I-climate-nature
to O
river B-climate-nature
runoff I-climate-nature
is O
expected O
to O
exceed O
the O
critical O
point O
. O

( O
4 O
) O
over O
the O
past O
10 O
years O
, O
the O
meltwater B-climate-nature
from O
permafrost B-climate-nature
has O
been O
as O
high O
as O
1.18 O
km3 O
/ O
a O
, O
which O
is O
equivalent O
to O
10 O
% O
of O
the O
annual B-climate-properties
runoff I-climate-properties
into O
the O
Qilian O
Mountain O
rivers B-climate-nature
. O

-DOCSTART- -X- O Oaf7235cd48d1cba89b4a00b01324766d

Results O
from O
this O
study O
are O
helpful O
not O
only O
in O
choosing O
a O
proper O
timescale O
but O
also O
in O
evaluating O
the O
futuristic O
drought B-climate-hazards
dynamics O
necessary O
for O
water B-climate-mitigations
resources I-climate-mitigations
planning I-climate-mitigations
and I-climate-mitigations
management I-climate-mitigations
. O

-DOCSTART- -X- O O089f41b09edf3c3be7521ffa902fd1cb

The O
100 O
- O
year O
return O
levels O
of O
joint O
storm B-climate-hazards
surges I-climate-hazards
and O
waves B-climate-nature
are O
used O
to O
map O
the O
spatial O
extent O
of O
flooding B-climate-hazards
in O
more O
than O
200 O
sandy B-climate-nature
beaches I-climate-nature
around O
the O
Balearic O
Islands O
by O
mid O
and O
late O
21st O
century O
, O
using O
the O
hydrodynamical O
LISFLOOD B-climate-models
- I-climate-models
FP I-climate-models
model O
and O
a O
high O
resolution O
( O
2 O
m O
) O
Digital O
Elevation O
Model.</p O
> O

-DOCSTART- -X- O O584ff65d06ec121aa2cda9a3bbad5adc

For O
experimental O
modeling O
, O
the O
laboratory O
model O
of O
wind B-climate-mitigations
- I-climate-mitigations
powered I-climate-mitigations
generator I-climate-mitigations
with O
a O
horizontal O
axis O
was O
used O
that O
operated O
as O
wind B-climate-mitigations
turbine I-climate-mitigations
in O
optimal O
mode O
. O

The O
obtained O
experimental O
data O
for O
the O
wake O
dynamics O
behind O
the O
model O
of O
wind O
- O
powered O
generator O
allowed O
ascertaining O
its O
impact O
on O
slowing O
down O
of O
incident O
vortex O
flow O
and O
determining O
the O
distance B-climate-properties
at O
which O
its O
impact O
on O
the O
stream O
disappears O
, O
and O
the O
deceleration O
values O
are O
comparable O
to O
the O
level O
of O
pulsations O
of O
incident O
flow O
. O

-DOCSTART- -X- O O651674baea0dc825cb5e22c08f400291

Rural O
households O
in O
Sub O
- O
Saharan O
Africa O
face O
several O
microeconomic O
challenges O
including O
food B-climate-impacts
crisis I-climate-impacts
of O
many O
dimensions O
. O

This O
research O
investigates O
the O
decisions O
agricultural O
households O
make O
in O
land B-climate-mitigations
and I-climate-mitigations
crop I-climate-mitigations
management I-climate-mitigations
, O
crop B-climate-mitigations
portfolio I-climate-mitigations
choices I-climate-mitigations
and O
postharvest O
storage O
to O
cope O
with O
the O
risks O
they O
encounter O
and O
improve O
their O
livelihood B-climate-assets
. O

-DOCSTART- -X- O O3e1a32b6f3707bd07ef2db837f4f5ca9

In O
a O
changing O
climate O
, O
the O
impact O
of O
tropical B-climate-hazards
cyclones I-climate-hazards
on O
the O
United O
States O
Atlantic O
and O
Gulf O
Coasts O
will O
be O
affected O
both O
by O
how O
intense O
and O
how O
frequent O
these O
storms B-climate-nature
become O
. O

Consistent O
with O
previous O
studies O
, O
we O
find O
that O
basin O
- O
wide O
and O
landfalling B-climate-hazards
tropical B-climate-hazards
cyclone I-climate-hazards
counts O
are O
significantly O
correlated O
with O
one O
another O
, O
lending O
further O
support O
for O
the O
use O
of O
paleohurricane B-climate-hazards
landfall B-climate-hazards
records O
to O
infer O
long O
- O
term O
basin O
- O
wide O
tropical B-climate-hazards
cyclone I-climate-hazards
trends O
. O

-DOCSTART- -X- O Oef0df818c6a445f55e97dde1ee87a2b3

The O
impacts O
of O
climate O
change O
on O
streamflow B-climate-properties
in O
the O
upper O
Yangtze O
River O
basin O
were O
studied O
using O
four O
hydrological B-climate-nature
models O
driven O
by O
bias O
- O
corrected O
climate O
projections O
from O
five O
General O
Circulation O
Models O
under O
four O
Representative O
Concentration O
Pathways O
. O

An O
analysis O
of O
variance O
( O
ANOVA O
) O
approach O
was O
used O
to O
quantify O
the O
uncertainty O
sources O
associated O
with O
the O
climate O
inputs O
and O
hydrological B-climate-nature
model O
structures O
. O

-DOCSTART- -X- O O952609930b2129c742dc28f9ddbba0e4

Frequent O
and O
increasingly O
severe O
flooding B-climate-hazards
in O
Ho O
Chi O
Minh O
City O
has O
raised O
people O
’s O
attention O
on O
the O
issue O
of O
climate O
change O
in O
recent O
years O
. O

However O
, O
the O
leading O
cause O
of O
today O
’s O
environmental O
risks O
is O
not O
entirely O
due O
to O
climate O
change O
but O
, O
more O
importantly O
, O
due O
to O
the O
out O
- O
of O
- O
control O
urban B-climate-problem-origins
growth I-climate-problem-origins
rate O
. O

This O
paper O
aims O
to O
improve O
the O
resilience O
of O
Ho O
Chi O
Minh O
City O
to O
floods B-climate-hazards
and O
reduce O
flood B-climate-impacts
damage I-climate-impacts
, O
and O
proposes O
four O
critical O
urban O
design O
principles O
: O

1 O
) O
Urban O
design O
must O
consider O
flooding B-climate-hazards
as O
an O
ecological O
resource O
; O

2 O
) O
Building O
flood B-climate-mitigations
storage I-climate-mitigations
space I-climate-mitigations
based O
on O
ecological B-climate-mitigations
infrastructure I-climate-mitigations
to O
adapt O
to O
dynamic O
changes O
of O
floods B-climate-hazards
. O

-DOCSTART- -X- O O33a385977ec7b1041a1f855c1ef95345

Ocean B-climate-nature
reanalyses O
are O
data O
assimilative O
simulations O
, O
aimed O
at O
estimating O
the O
four O
- O
dimensional O
state O
of O
the O
ocean B-climate-nature
over O
long O
periods O
, O
in O
a O
way O
as O
consistent O
over O
time O
as O
possible O
. O

An O
upgraded O
version O
of O
the O
Euro B-climate-organizations
- I-climate-organizations
Mediterranean I-climate-organizations
Center I-climate-organizations
for I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
( O
CMCC B-climate-organizations
) O
eddy O
- O
permitting O
global O
ocean B-climate-nature
reanalysis O
, O
named O
CMCC B-climate-models
Global I-climate-models
Ocean I-climate-models
Reanalysis I-climate-models
System I-climate-models
( O
C B-climate-models
- I-climate-models
GLORS I-climate-models
) O
version O
4 O
, O
was O
recently O
released O
. O

-DOCSTART- -X- O Ob0a23caa2ba77dc447d4cdaf34ba0f00

Pakistan O
is O
currently O
facing O
physical O
and O
economic O
water B-climate-hazards
scarcity I-climate-hazards
issues O
that O
are O
further O
complicated O
by O
the O
rapid O
increase O
in O
its O
population O
and O
by O
climate O
change O
. O

Many O
studies O
have O
focused O
on O
the O
physical O
water B-climate-hazards
scarcity I-climate-hazards
using O
hydrological B-climate-nature
modeling O
and O
the O
measurement O
of O
the O
impact O
of O
climate O
change O
on O
water B-climate-nature
resources I-climate-nature
in O
the O
Upper O
Indus O
Basin O
( O
UIB O
) O
. O

However O
, O
few O
studies O
have O
concentrated O
on O
the O
importance O
of O
the O
economic O
water B-climate-hazards
scarcity I-climate-hazards
, O
that O
is O
, O
the O
water B-climate-mitigations
management I-climate-mitigations
issue O
under O
the O
looming O
impacts O
of O
climate O
change O
and O
the O
population B-climate-problem-origins
explosion I-climate-problem-origins
of O
Pakistan O
. O

The O
purpose O
of O
this O
study O
is O
to O
develop O
a O
management O
strategy O
which O
helps O
to O
achieve O
water B-climate-assets
security I-climate-assets
and O
sustainability O
in O
the O
Upper O
Indus O
Basin O
( O
UIB O
) O
with O
the O
help O
of O
different O
socio O
- O
economic O
and O
climate O
change O
scenarios O
using O
WEAP B-climate-models
( O
Water B-climate-models
Evaluation I-climate-models
and I-climate-models
Planning I-climate-models
) O
modeling O
. O

This O
study O
further O
explores O
the O
importance O
of O
proposed O
dams B-climate-mitigations
( O
likely O
to O
be O
built O
until O
2025 O
) O
by O
WAPDA B-climate-organizations
( O
Water B-climate-organizations
and I-climate-organizations
Power I-climate-organizations
Development I-climate-organizations
Authority I-climate-organizations
) O
. O

-DOCSTART- -X- O O5b79665f46ea08f4097596c833058432

In O
the O
present O
study O
, O
we O
use O
ensemble O
of O
statistically O
downscaled O
precipitation B-climate-nature
and O
temperature B-climate-properties
from O
various O
models O
. O

The O
dataset O
used O
is O
multi O
- O
model O
ensemble O
of O
10 O
global O
climate O
models O
( O
GCMs O
) O
downscaled O
product O
from O
CMIP5 B-climate-models
daily O
dataset O
using O
the O
Bias O
Correction O
and O
Spatial O
Downscaling O
( O
BCSD O
) O
technique O
, O
generated O
at O
Portland B-climate-organizations
State I-climate-organizations
University I-climate-organizations
. O

The O
multi O
- O
model O
ensemble O
of O
both O
precipitation B-climate-nature
and O
temperature B-climate-properties
is O
evaluated O
for O
dry O
and O
wet O
periods O
for O
10 O
sub B-climate-nature
- I-climate-nature
basins I-climate-nature
across O
Columbia O
River O
Basin O
( O
CRB O
) O
. O

-DOCSTART- -X- O Oa45b371926e8b2ba26c969fb63eed7e3

We O
investigate O
the O
effects O
of O
promoting O
simple O
climate B-climate-mitigations
- I-climate-mitigations
friendly I-climate-mitigations
diet I-climate-mitigations
recommendations O
in O
Denmark O
, O
Finland O
and O
France O
, O
with O
the O
objectives O
of O
identifying O
recommendations O
that O
lower O
greenhouse O
gas O
emissions O
, O
improve O
public O
health B-climate-assets
, O
and O
are O
cost B-climate-assets
- I-climate-assets
beneficial I-climate-assets
. O

The O
five O
recommendations O
considered O
in O
the O
analysis O
focus O
on O
consumption O
of O
fruits B-climate-assets
and O
vegetables B-climate-assets
, O
red B-climate-problem-origins
meat I-climate-problem-origins
, O
all O
meat B-climate-problem-origins
and O
all O
animal B-climate-problem-origins
products I-climate-problem-origins
, O
as O
well O
as O
the O
greenhouse O
gas O
emissions O
arising O
from O
the O
diet O
. O

-DOCSTART- -X- O O8d45d9d70761d2b5088f1f4f28e78f9d

Utilizing O
vegetation B-climate-nature
indicators O
derived O
from O
remotely O
sensed O
imagery O
, O
we O
present O
an O
approach O
to O
forecast O
shifts O
in O
the O
future O
distribution O
of O
vegetation B-climate-nature
. O

In O
this O
paper O
we O
developed O
models O
between O
a O
historical O
time O
series O
of O
Advanced B-climate-observations
Very I-climate-observations
High I-climate-observations
Resolution I-climate-observations
Radiometer I-climate-observations
( O
AVHRR B-climate-observations
) O
satellite O
imagery O
from O
1987 O
to O
2007 O
at O
1 O
km O
spatial O
resolution O
with O
corresponding O
climate O
data O
using O
regression O
tree O
modeling O
approaches O
. O

We O
then O
applied O
these O
models O
to O
three O
climate O
change O
scenarios O
produced O
by O
the O
Canadian B-climate-organizations
Centre I-climate-organizations
for I-climate-organizations
Climate I-climate-organizations
Modeling I-climate-organizations
and I-climate-organizations
Analysis I-climate-organizations
( O
CCCma B-climate-organizations
) O
to O
predict O
and O
map O
productivity O
indices O
in O
2065 O
. O

The O
Coast O
Mountains O
of O
the O
Pacific O
Maritime O
region O
and O
high O
elevation O
edge O
habitats B-climate-organisms
across O
British O
Columbia O
were O
forecasted O
to O
experience O
the O
greatest O
amount O
of O
change O
. O

-DOCSTART- -X- O Oa031fc9fe2e751089e3847a5e8d12d24

Feedback O
mechanisms O
between O
soil B-climate-properties
moisture I-climate-properties
dynamics O
and O
meteorological O
influences O
are O
key O
factors O
when O
it O
comes O
to O
understanding O
the O
occurrence O
of O
drought B-climate-hazards
events O
. O

We O
used O
long O
- O
term O
high O
- O
resolution O
measurements O
of O
soil B-climate-properties
moisture I-climate-properties
on O
a O
large O
inclined O
lysimeter O
at O
a O
test O
site O
near O
Karlsruhe O
, O
Germany O
. O

The O
measurements O
indicate O
( O
i O
) O
a O
seasonal O
evaporation B-climate-properties
depth I-climate-properties
of O
over O
two O
meters O
. O

Largest O
changes O
occur O
at O
tipping O
points O
during O
years O
of O
extreme O
drought B-climate-hazards
, O
with O
significant O
changes O
to O
the O
subsequent O
soil B-climate-properties
moisture I-climate-properties
levels O
. O

The O
study O
highlights O
the O
importance O
of O
soil B-climate-properties
moisture I-climate-properties
measurements O
for O
the O
understanding O
of O
soil B-climate-properties
moisture I-climate-properties
fluxes O
in O
the O
vadose B-climate-nature
zone I-climate-nature
. O

-DOCSTART- -X- O O53f5837586d6c0238ae896bbeaa95751

The O
Middle O
- O
Late O
Holocene O
transition O
around O
2,500 O
B.C. O
is O
one O
of O
the O
defining O
episodes O
of O
regional O
landscape O
changes O
in O
the O
Southeastern O
United O
States O
area O
and O
throughout O
the O
world O
. O

The O
influences O
include O
variations O
in O
the O
earth O
's O
rotational B-climate-nature
tilt I-climate-nature
, O
solar B-climate-nature
emissions I-climate-nature
, O
global O
- O
scale O
volcanism B-climate-hazards
, O
and O
atmospheric O
chemistry O
. O

-DOCSTART- -X- O O750591ea4e900f2a208d4f40db085b14

We O
investigate O
the O
qualitative O
properties O
of O
the O
climate O
- O
economic O
growth O
model O
introduced O
by O
Brock O
et O
al O
. O
, O
( O
2012 O
) O
. O

We O
assume O
that O
the O
mean O
annual O
distribution O
of O
solar B-climate-properties
radiation I-climate-properties
energy O
and O
the O
fraction O
of O
incoming O
radiation B-climate-properties
flux I-climate-properties
absorbed O
by O
the O
surface B-climate-nature
have O
a O
specific O
form O
, O
and O
perform O
a O
rigorous O
mathematical O
analysis O
when O
the O
time O
scale O
for O
temperature B-climate-properties
is O
taken O
to O
be O
faster O
than O
that O
of O
carbon O
. O

-DOCSTART- -X- O O401c231b953382dd640b4f52e2ce20e9

Stimulating O
renewable B-climate-mitigations
energy I-climate-mitigations
is O
a O
crucial O
objective O
in O
view O
of O
tackling O
climate O
change O
and O
coping O
with O
future O
fossil B-climate-problem-origins
fuel I-climate-problem-origins
scarcity O
. O

In O
France O
, O
fuelwood B-climate-mitigations
appears O
to O
be O
an O
important O
source O
for O
the O
renewable B-climate-mitigations
energy I-climate-mitigations
mix O
. O

Using O
the O
French B-climate-models
Forest I-climate-models
Sector I-climate-models
Model I-climate-models
, O
our O
paper O
aims O
to O
assess O
the O
impacts O
of O
three O
policy O
options O
to O
stimulate O
fuelwood B-climate-mitigations
consumption O
: O
a O
consumer O
subsidy O
, O
a O
producer O
subsidy O
and O
a O
fixed O
- O
demand O
contract O
policy O
. O

-DOCSTART- -X- O Ob036041d92e89a7f1f24bf2e85be1cfa

Cotton B-climate-assets
production O
is O
highly O
vulnerable O
to O
climate O
change O
, O
and O
heat B-climate-hazards
stress I-climate-hazards
is O
a O
major O
constraint O
in O
the O
cotton B-climate-assets
zone O
of O
Punjab O
, O
Pakistan O
. O

After O
the O
calibration O
and O
validation O
against O
field O
data O
, O
the O
Cropping B-climate-models
System I-climate-models
Model I-climate-models
CSM B-climate-models
– I-climate-models
CROPGRO I-climate-models
– I-climate-models
Cotton I-climate-models
in O
the O
shell O
of O
the O
decision B-climate-models
support I-climate-models
system I-climate-models
for I-climate-models
agro I-climate-models
- I-climate-models
technology I-climate-models
transfer I-climate-models
( O
DSSAT B-climate-models
) O
was O
run O
with O
a O
future O
climate O
generated O
under O
two O
representative O
concentration O
pathways O
( O
RCPs O
) O
, O
viz O
. O

RCPs B-climate-datasets
4.5 I-climate-datasets
and I-climate-datasets
8.5 I-climate-datasets
with O
five O
global O
circulation O
models O
( O
GCMs O
) O
. O

-DOCSTART- -X- O Oc959eaa195280a5b07ab3fffe9665628

Heihe O
River O
Basin O
is O
the O
second O
largest O
inland O
river B-climate-nature
basin I-climate-nature
in O
China O
, O
where O
water B-climate-assets
supply I-climate-assets
service O
in O
the O
upper O
reach O
has O
greater O
influence O
on O
the O
sustainable O
development O
of O
middle O
and O
lower O
reaches O
. O

This O
study O
analyzed O
the O
influence O
of O
land B-climate-problem-origins
use I-climate-problem-origins
/ I-climate-problem-origins
land I-climate-problem-origins
cover I-climate-problem-origins
change I-climate-problem-origins
( O
LUCC B-climate-problem-origins
) O
on O
the O
water B-climate-assets
supply I-climate-assets
service O
in O
the O
upper O
reach O
by O
carrying O
out O
scenario O
simulation O
. O

Thereafter O
three O
scenarios O
( O
precipitation B-climate-nature
change O
and O
LUCC B-climate-problem-origins
change O
combined O
, O
LUCC B-climate-problem-origins
change O
only O
, O
and O
precipitation B-climate-nature
change O
only O
) O
were O
established O
to O
analyze O
the O
impacts O
of O
LUCC B-climate-problem-origins
and O
precipitation B-climate-nature
change O
on O
the O
water B-climate-properties
yield I-climate-properties
. O

-DOCSTART- -X- O O880034977a4d6d576f35f22150a7e0ca

The O
annual B-climate-properties
PDI I-climate-properties
( O
APDI B-climate-properties
) O
in O
the O
region O
is O
calculated O
as O
the O
sum O
of O
the O
PDI B-climate-properties
, O
defined O
as O
the O
cube O
of O
the O
maximum B-climate-properties
sustained I-climate-properties
wind I-climate-properties
speed I-climate-properties
at O
landfall B-climate-hazards
of O
each O
tropical B-climate-hazards
cyclone I-climate-hazards
( O
TC B-climate-hazards
) O
making O
landfall B-climate-hazards
at O
that O
region O
. O

The O
ENSO B-climate-nature
and O
basin O
- O
wide O
mode O
represents O
the O
PDI B-climate-properties
patterns O
associated O
with O
ENSO B-climate-nature
events O
and O
the O
overall B-climate-properties
PDI I-climate-properties
over O
the O
WNP O
. O

Based O
on O
the O
steering O
flow O
( O
average O
winds B-climate-nature
within O
the O
850–300 O
hPa O
layer O
) O
near O
the O
East O
Asian O
coast O
, O
a O
three O
- O
cell O
model O
for O
TC O
landfall O
in O
East O
Asia O
is O
proposed O
, O
which O
corresponds O
to O
three O
major O
modes O
of O
the O
atmospheric B-climate-nature
circulation I-climate-nature
in O
the O
WNP O
. O

-DOCSTART- -X- O O2acaf01b385ef13a51e216e2442c6e12

As O
part O
of O
a O
study O
into O
the O
potential O
impact O
of O
climate O
change O
on O
Thailand O
’s O
electricity B-climate-assets
demand O
it O
has O
been O
necessary O
to O
find O
an O
efficient O
and O
effective O
way O
of O
linking O
climate O
to O
demand O
levels O
whilst O
minimising O
data O
requirements O
. O

-DOCSTART- -X- O O35341c31e1bc504655559c076e2e90d5

In O
this O
research O
we O
will O
simulate O
the O
Canadian O
Great O
Lakes O
basin O
using O
SWAT B-climate-models
model O
. O

The O
model O
will O
be O
used O
to O
identify O
the O
major O
NPS B-climate-hazards
pollution I-climate-hazards
contributing O
watersheds B-climate-nature
. O

Additionally O
, O
scenarios O
of O
NPS B-climate-hazards
pollution I-climate-hazards
mitigation B-climate-mitigations
methods O
will O
be O
investigated O
to O
identify O
the O
most O
cost O
effective O
BMPs B-climate-mitigations
. O

In O
addition O
, O
the O
tool O
has O
the O
potential O
to O
aid O
into O
the O
focus O
of O
the O
Great O
Lakes O
Commission O
on O
reducing O
phosphorus O
loads O
to O
the O
lakes B-climate-nature
. O

-DOCSTART- -X- O Od03f10085d8fe7c6a2db2f5f74dbd57a

Abstract O
The O
impact O
of O
a O
port B-climate-assets
disruption B-climate-impacts
can O
reverberate O
across O
the O
entire O
economy O
through O
global O
supply B-climate-assets
chains I-climate-assets
. O

It O
is O
essential O
to O
ensure O
the O
regular O
operation O
of O
seaborne B-climate-assets
transportation I-climate-assets
considering O
all O
risks O
. O

This O
paper O
proposes O
an O
approach O
to O
evaluate O
the O
economic O
losses O
due O
to O
port O
disruptions B-climate-impacts
caused O
by O
typhoon B-climate-hazards
- O
induced O
wind B-climate-nature
disasters O
. O

-DOCSTART- -X- O O65a0dd0caaa3702a4b6b3cd7527fa8c9

We O
summarise O
modelling O
studies O
of O
the O
most O
economically O
important O
cassava B-climate-assets
diseases B-climate-impacts
and O
arthropods B-climate-organisms
, O
highlighting O
research O
gaps O
where O
modelling O
can O
contribute O
to O
the O
better O
management O
of O
these O
in O
the O
areas O
of O
surveillance O
, O
control O
, O
and O
host O
- O
pest B-climate-hazards
dynamics O
understanding O
the O
effects O
of O
climate O
change O
and O
future O
challenges O
in O
modelling O
. O

-DOCSTART- -X- O O4f2379a5f516efc727c865803eaabbe9

In O
this O
study O
we O
combine O
information O
from O
landscape O
characteristics O
, O
demographic O
inference O
and O
species B-climate-organisms
distribution O
modelling O
to O
identify O
environmental O
factors O
that O
shape O
the O
genetic O
distribution O
of O
the O
fossorial O
rodent B-climate-organisms
Ctenomys B-climate-organisms
. O

A O
core O
region O
of O
stable B-climate-properties
suitable O
habitat B-climate-organisms
was O
identified O
from O
the O
Last O
Interglacial O
, O
which O
is O
projected O
to O
remain O
stable B-climate-properties
into O
the O
future O
. O

This O
region O
is O
also O
the O
most O
genetically B-climate-organisms
diverse I-climate-organisms
and O
is O
currently O
under O
strong O
anthropogenic O
pressure O
. O

Results O
reveal O
complex O
demographic O
dynamics O
, O
which O
have O
been O
in O
constant O
change O
in O
both O
time O
and O
space O
, O
and O
are O
likely O
linked O
to O
the O
evolution O
of O
the O
Paraná O
River O
. O

The O
protection O
of O
this O
core O
stable B-climate-properties
habitat B-climate-organisms
is O
of O
prime O
importance O
given O
the O
increasing O
levels O
of O
human O
disturbance O
across O
this O
wetland B-climate-nature
system O
and O
the O
threat O
of O
climate O
change O
. O

-DOCSTART- -X- O Oa1b73110bec023c3afc9c8a81b6d799d

Tropical B-climate-hazards
cyclones I-climate-hazards
are O
one O
of O
the O
major O
reasons O
for O
catastrophic O
damage B-climate-impacts
in O
low O
- O
altitude O
coastal B-climate-nature
regions I-climate-nature
. O

Our O
aim O
is O
to O
study O
the O
characteristics O
of O
the O
high O
- O
intensity O
tropical B-climate-hazards
cyclones I-climate-hazards
using O
a O
scientific O
application O
, O
named O
COAWST B-climate-models
, O
designed O
to O
understand O
the O
coastal B-climate-nature
changes O
caused O
by O
the O
natural O
processes O
and O
to O
develop O
a O
faster O
model O
to O
predict O
the O
probable O
damage B-climate-impacts
with O
proper O
warning O
and O
with O
adequate O
accuracy O
on O
the O
Earth B-climate-models
Simulator I-climate-models
which O
is O
known O
for O
one O
of O
its O
kind O
in O
the O
efficient O
simulation O
of O
the O
natural O
phenomenon O
using O
its O
high O
- O
performance O
vector O
processor O
. O

-DOCSTART- -X- O O7331eb1137e6c5582fcb19ceebd78bc3

The O
purpose O
of O
this O
fact O
sheet O
is O
to O
explain O
the O
need O
for O
using O
emerging O
knowledge O
about O
climate O
change O
and O
tree B-climate-organisms
genetics O
to O
guide O
post O
- O
disturbance O
restoration O
of O
ponderosa B-climate-organisms
pine I-climate-organisms
in O
the O
Southwest O
. O

-DOCSTART- -X- O Oefbe49964c430f3d0fa375725163ac02

The O
dynamics O
of O
the O
rainfall B-climate-properties
- I-climate-properties
runoff I-climate-properties
processes O
are O
complex O
and O
variable O
both O
spatially O
and O
temporally O
. O

There O
is O
a O
rich O
literature O
on O
physical O
representation O
of O
streamflow B-climate-properties
generation O
processes O
, O
such O
as O
saturation O
excess O
overland B-climate-nature
flow I-climate-nature
, O
often O
at O
small O
scales O
. O

Yet O
, O
continental O
- O
scale O
estimations O
of O
the O
streamflow B-climate-properties
generation O
processes O
in O
zones O
with O
shallow O
groundwater B-climate-nature
systems O
are O
still O
poor O
. O

This O
has O
led O
to O
inability O
of O
earth O
system O
models O
or O
large O
- O
scale O
hydrologic B-climate-nature
models O
to O
correctly O
simulate O
stream B-climate-properties
flows I-climate-properties
at O
( O
un)gauged O
basins B-climate-nature
with O
high O
potential O
for O
the O
presence O
of O
saturation O
excess O
overland B-climate-nature
flow I-climate-nature
. O

We O
have O
evaluated O
and O
tested O
the O
ability O
of O
these O
indices O
in O
locating O
high O
- O
resolution O
zones O
of O
shallow O
groundwater B-climate-nature
against O
in O
- O
situ O
observations O
of O
water B-climate-nature
table I-climate-nature
depth O
. O

Furthermore O
, O
as O
a O
significant O
part O
of O
incoming O
precipitation B-climate-nature
is O
transformed O
to O
overland B-climate-nature
flow I-climate-nature
due O
to O
oversaturation B-climate-nature
, O
these O
datasets O
could O
be O
introduced O
as O
a O
useful O
indicator O
of O
areas O
with O
flood B-climate-hazards
and O
erosion B-climate-hazards
susceptibility O
. O

-DOCSTART- -X- O Ofd03cefe32902265cab9a7997b5642e9

Empirical O
- O
statistical O
downscaling O
( O
ESD O
) O
methods O
coupled O
to O
output O
from O
climate O
model O
projections O
are O
promising O
tools O
to O
assess O
impacts O
at O
regional O
to O
local O
scale O
. O

However O
, O
most O
ESD O
methods O
require O
long O
observational O
time O
series O
at O
the O
target O
sites O
, O
and O
this O
often O
restricts O
robust O
impact O
assessments O
to O
a O
small O
number O
of O
sites O
. O

The O
approach O
is O
based O
on O
the O
well O
- O
established O
quantile O
mapping O
method O
and O
incorporates O
two O
major O
steps O
: O
( O
1 O
) O
climate O
model O
bias O
correction O
to O
the O
most O
representative O
station O
with O
long O
- O
term O
measurements O
and O
( O
2 O
) O
spatial O
transfer O
of O
bias O
- O
corrected O
model O
data O
to O
represent O
target O
site O
characteristics O
. O

The O
method O
's O
applicability O
is O
validated O
using O
( O
1 O
) O
long O
- O
term O
weather O
stations O
across O
the O
topographically B-climate-nature
and O
climatologically O
complex O
territory O
of O
Switzerland O
and O
( O
2 O
) O
sparse O
data O
sets O
from O
Swiss O
permafrost B-climate-nature
research O
sites O
located O
in O
challenging O
conditions O
at O
high O
altitudes B-climate-properties
. O

-DOCSTART- -X- O Occ89e7e7f7462dde3f031203e067b09a

A O
major O
effect O
of O
climatic O
change O
is O
the O
global O
increase O
in O
forest B-climate-hazards
fires I-climate-hazards
, O
which O
potentially O
creates O
an O
increase O
in O
food B-climate-assets
availability O
for O
herbivorous B-climate-organisms
species I-climate-organisms
. O

This O
cascading O
effect O
of O
forest B-climate-hazards
fires I-climate-hazards
might O
have O
implications O
on O
future O
ecosystem O
functioning O
in O
the O
burned B-climate-properties
area I-climate-properties
, O
and O
more O
knowledge O
about O
the O
effects O
of O
landscape O
features O
on O
predator‐prey B-climate-organisms
interactions I-climate-organisms
is O
needed O
to O
adapt O
conservation O
and O
wildlife B-climate-organisms
management O
policies O
, O
to O
the O
changing O
climate O
. O

-DOCSTART- -X- O Oa7aa1ab9397effcf6a1ea52c896003f3

I O
tested O
these O
predictions O
by O
conducting O
a O
correlative O
cross‐sectional O
study O
in O
three O
different O
boreal B-climate-nature
forests I-climate-nature
in O
the O
north O
of O
Sweden O
, O
each O
with O
a O
burned B-climate-hazards
site I-climate-hazards
that O
burned O
in O
2006 O
and O
an O
equal O
sized O
unburned O
control O
site O
. O

The O
herbivore B-climate-organisms
community O
there O
is O
predominantly O
comprised O
of O
moose B-climate-organisms
and O
mountain B-climate-organisms
hare I-climate-organisms
. O

Measurements O
on O
species B-climate-organisms
passage O
rates O
and O
the O
time O
they O
spend O
in O
front O
of O
the O
camera O
are O
derived O
from O
footage O
obtained O
from O
remotely O
triggered O
cameras O
with O
a O
PIR O
sensor O
. O

-DOCSTART- -X- O O792a78604862e2b84da53dbfcb268c90

The O
impact O
of O
climate O
change O
on O
bushfires B-climate-hazards
is O
predicted O
to O
completely O
transform O
fire B-climate-hazards
regimes I-climate-hazards
resulting O
in O
strong O
ecological O
disturbances O
in O
many O
wildlife B-climate-organisms
systems O
, O
and O
a O
huge O
stress O
across O
many O
taxa B-climate-organisms
. O

Despite O
being O
the O
most O
declining O
vertebrate B-climate-organisms
group O
, O
amphibians B-climate-organisms
are O
underrepresented O
in O
fire B-climate-hazards
ecology O
research O
. O

Amongst O
these O
Pseudophryne B-climate-organisms
semimarmorata I-climate-organisms
a O
small O
toadlet B-climate-organisms
endemic B-climate-organisms
to O
south O
- O
eastern O
Australia O
, O
is O
particularly O
under O
- O
researched O
. O

Twenty O
- O
three O
sites O
were O
surveyed O
using O
Song B-climate-observations
Meter I-climate-observations
SM4 I-climate-observations
Acoustic O
Recorders O
, O
which O
were O
placed O
on O
the O
western O
end O
of O
the O
park O
, O
and O
50 O
on O
the O
eastern O
end O
using O
15 O
minute O
callplayback O
surveys O
. O

Detection O
and O
occupancy O
modelling O
were O
used O
to O
determine O
the O
impact O
of O
survey O
covariates O
( O
temperature B-climate-properties
, O
humidity B-climate-properties
, O
in B-climate-properties
situe I-climate-properties
wind I-climate-properties
and O
maximum B-climate-properties
wind I-climate-properties
and O
rainfall B-climate-nature
) O
and O
site O
covariates O
( O
vegetation B-climate-nature
structure O
, O
time B-climate-properties
since I-climate-properties
fire I-climate-properties
, O
number B-climate-properties
of I-climate-properties
fires I-climate-properties
and O
road B-climate-properties
density I-climate-properties
) O
on O
the O
detection O
and O
occurrence O
of O
P.semimarmorata B-climate-organisms
and O
other O
frogs B-climate-organisms
heard O
. O

Overall O
, O
we O
were O
able O
to O
test O
the O
impact O
of O
fire B-climate-hazards
on O
3 O
frogs B-climate-organisms
: O
Pseudophryne B-climate-organisms
semimarmorata I-climate-organisms
, O
Geocrinia B-climate-organisms
victoriana I-climate-organisms
and O
Litoria B-climate-organisms
ewingii I-climate-organisms
. O

P.semimarmorata B-climate-organisms
and O
G.victoriana B-climate-organisms
had O
a O
significantly O
negative O
relationship O
with O
roads B-climate-assets
, O
and O
L.ewingii B-climate-organisms
was O
significantly O
impacted O
by O
vegetation B-climate-nature
structure O
showing O
a O
preference O
for O
canopy B-climate-properties
cover I-climate-properties
and O
leaf B-climate-nature
litter I-climate-nature
. O

-DOCSTART- -X- O Oe90c363166d4dc1d13962cf4c0a794ed

Many O
studies O
have O
identified O
climate O
warming O
to O
be O
among O
the O
most O
important O
threats O
to O
biodiversity B-climate-organisms
. O

Climate O
change O
is O
expected O
to O
have O
stronger O
effects O
on O
species B-climate-organisms
with O
low O
genetic B-climate-organisms
diversity I-climate-organisms
, O
ectothermic B-climate-properties
physiology I-climate-properties
, O
small O
ranges O
, O
low O
effective O
populations B-climate-properties
sizes I-climate-properties
, O
specific O
habitat B-climate-organisms
requirements O
and O
limited O
dispersal B-climate-properties
capabilities I-climate-properties
. O

-DOCSTART- -X- O O10c23e0390e16693b6f779c5310783a2

Natural O
aerosols B-climate-nature
are O
a O
key O
component O
of O
many O
biogeochemical B-climate-nature
cycles I-climate-nature
, O
they O
define O
the O
baseline O
from O
which O
the O
pre‒industrial O
to O
present‒day O
anthropogenic O
aerosol B-climate-nature
radiative B-climate-properties
forcing I-climate-properties
is O
calculated O
, O
and O
they O
dominate O
the O
net O
effect O
of O
all O
aerosols B-climate-nature
on O
the O
incoming O
solar B-climate-properties
radiation I-climate-properties
. O

-DOCSTART- -X- O O2055457d1a7cc4e77534b20277e89c85

When O
pre O
- O
industrial O
fire B-climate-nature
emissions I-climate-nature
from O
two O
global O
fire B-climate-hazards
models O
are O
implemented O
in O
a O
global O
aerosol B-climate-nature
model O
, O
pre O
- O
industrial O
global O
mean O
cloud B-climate-nature
condensation I-climate-nature
nuclei I-climate-nature
concentrations B-climate-properties
increase O
by O
a O
factor O
1.6 O
- O
2.7 O
relative O
to O
the O
widely O
used O
AeroCom B-climate-datasets
dataset O
. O

-DOCSTART- -X- O Odda02c067a11b2e3e92a0c8067bb70aa

Assessment O
of O
irrigation B-climate-mitigations
water O
requirement O
( O
IWR O
) O
is O
a O
prerequisite O
for O
planning O
and O
management O
of O
an O
irrigation B-climate-mitigations
scheme O
, O
particularly O
for O
a O
water O
short O
scheme O
. O

In O
this O
context O
, O
this O
study O
was O
conducted O
to O
estimate O
the O
current O
and O
future O
IWR O
under O
A2 B-climate-datasets
( O
very O
heterogeneous O
world O
) O
and O
B2 B-climate-datasets
( O
world O
in O
which O
emphasis O
is O
on O
local O
solutions O
to O
economic O
, O
social O
, O
and O
environmental O
sustainability O
) O
scenarios O
of O
IPCC B-climate-organizations
emission O
for O
Hakwatuna O
Oya O
irrigation B-climate-mitigations
scheme O
using O
SDSM B-climate-models
and O
CROPWAT B-climate-models
models O
. O

-DOCSTART- -X- O Od06a261ed7b20e63d58b4863b2bf3d6b

The O
significant O
increase O
in O
the O
world O
population O
living O
within O
close O
proximity O
to O
coastlines B-climate-nature
has O
assigned O
further O
importance O
to O
coastal B-climate-mitigations
protection I-climate-mitigations
structures O
. O

This O
research O
provides O
an O
integrated O
model O
for O
the O
optimisation O
of O
maintenance O
and O
repair B-climate-mitigations
for O
rubble O
- O
mound O
breakwaters B-climate-mitigations
, O
revetments B-climate-mitigations
and O
groins B-climate-mitigations
under O
simulated O
climatic O
conditions O
. O

The O
model O
starts O
by O
establishing O
an O
Asset O
Inventory O
Database O
( O
AID O
) O
, O
a O
Markov O
- O
Chain O
( O
MC O
) O
Deterioration O
Engine O
, O
and O
a O
Genetic O
Algorithm O
( O
GA O
) O
repair B-climate-mitigations
and O
maintenance O
Optimisation O
Engine O
. O

The O
case O
study O
consists O
of O
a O
group O
of O
rubble B-climate-nature
- I-climate-nature
mound I-climate-nature
structures O
in O
Alexandria O
, O
Egypt O
. O

-DOCSTART- -X- O O0206a04db1eae7a0df2ad4944a6edae3

temperatures B-climate-properties
over O
the O
Arctic O
region O
have O
been O
increasing O
twice O
as O
fast O
as O
global B-climate-properties
mean I-climate-properties
temperatures I-climate-properties
, O
a O
phenomenon O
known O
as O
arctic B-climate-properties
amplification I-climate-properties
. O

One O
main O
contributor O
to O
this O
polar O
warming O
is O
the O
large O
decline O
of O
Arctic O
sea B-climate-nature
ice I-climate-nature
observed O
since O
the O
beginning O
of O
satellite O
observations O
, O
which O
has O
been O
attributed O
to O
the O
increase O
of O
greenhouse O
gases O
. O

There O
is O
considerable O
variability O
in O
the O
spatial O
extent O
of O
ice B-climate-nature
cover I-climate-nature
on O
seasonal O
, O
interannual O
and O
decadal O
time O
scales O
. O

-DOCSTART- -X- O Oc0c68d6f349f2f2d5c3080dbf764f9dc

Abstract O
: O
We O
assessed O
the O
relative O
hydrological O
impacts O
of O
climate O
change O
and O
urbanization B-climate-problem-origins
using O
an O
integrated O
approach O
that O
links O
the O
statistical O
downscaling O
model O
( O
SDSM B-climate-models
) O
, O
the O
Hydrological B-climate-models
Simulation I-climate-models
Program I-climate-models
— I-climate-models
Fortran I-climate-models
( O
HSPF B-climate-models
) O
and O
the O
impervious B-climate-models
cover I-climate-models
model I-climate-models
( O
ICM B-climate-models
) O
. O

-DOCSTART- -X- O O5743830cea61ece75cebd939f35f4384

To O
upscale O
the O
genetic O
parameters O
of O
CERES B-climate-models
- I-climate-models
Rice I-climate-models
in O
regional O
applications O
, O
Jiangsu O
Province O
, O
the O
second O
largest O
rice B-climate-assets
producing O
province O
in O
China O
, O
was O
taken O
as O
an O
example O
. O

Then O
the O
eight O
genetic O
parameters O
of O
CERES B-climate-models
- I-climate-models
Rice I-climate-models
, O
particularly O
the O
four O
parameters O
related O
to O
the O
yield B-climate-assets
, O
were O
modified O
and O
validated O
using O
the O
Trial O
and O
Error O
Method O
and O
the O
local O
statistical O
data O
of O
rice B-climate-assets
yield O
at O
a O
county O
level O
from O
2001 O
to O
2004 O
, O
combined O
with O
the O
regional O
experiments O
of O
rice B-climate-assets
varieties O
in O
the O
province O
as O
well O
as O
the O
local O
meteorological O
and O
soil O
data O
( O
Method O
1 O
) O
. O

-DOCSTART- -X- O O04fc50c820d7c1316e176e96afb09655

As O
the O
global O
climate O
changes O
, O
biological B-climate-organisms
populations I-climate-organisms
have O
to O
adapt O
in O
place O
or O
move O
in O
space O
to O
stay O
within O
their O
preferred B-climate-properties
temperature I-climate-properties
regime I-climate-properties
. O

We O
present O
a O
mathematical O
tool O
to O
study O
transient O
behaviour O
of O
population O
dynamics O
within O
such O
moving B-climate-hazards
habitats I-climate-hazards
to O
discern O
between O
populations O
at O
high O
and O
low O
risk O
of O
extinction B-climate-hazards
. O

-DOCSTART- -X- O Oaf5dc60c0ccba450cff53b1c5120b185

Abstract O
That O
pollen B-climate-nature
and O
sedimentological O
evidence O
can O
make O
a O
significant O
contribution O
to O
our O
understanding O
of O
the O
nature O
and O
antiquity O
of O
agricultural B-climate-assets
development O
in O
the O
highlands B-climate-nature
of O
New O
Guinea O
has O
long O
been O
recognised O
and O
promoted O
by O
Jack O
Golson O
. O

Five O
palaeoecological O
sites O
from O
highland B-climate-nature
valleys O
( O
1400 O
- O
1890 O
m O
altitude B-climate-properties
) O
that O
cover O
the O
period O
from O
the O
last O
glacial O
maximum O
( O
22 O
000 O
cal O
BP O
) O
to O
the O
present O
are O
reviewed O
and O
the O
implications O
of O
the O
rate O
and O
direction O
of O
environmental O
changes O
are O
evaluated O
. O

-DOCSTART- -X- O O6dabd69dcf0a6b72a5c8becbf45b6fa4

While O
the O
economic O
returns O
to O
using O
chemical O
fertilizer B-climate-assets
in O
Africa O
can O
be O
large O
, O
application O
rates O
are O
low O
. O

-DOCSTART- -X- O O2de4896543df769ce24d00c71da54908

Here O
we O
present O
a O
macro O
- O
scale O
analysis O
of O
climate O
change O
impacts O
on O
terrestrial B-climate-nature
ecosystems I-climate-nature
based O
on O
newly O
developed O
sets O
of O
climate O
scenarios O
featuring O
a O
step O
- O
wise O
sampling O
of O
global B-climate-properties
mean I-climate-properties
temperature I-climate-properties
increase O
between O
1.5 O
and O
5 O
K O
by O
2100 O
. O

These O
are O
processed O
by O
a O
biogeochemical B-climate-nature
model O
( O
LPJmL B-climate-models
) O
to O
derive O
an O
aggregated O
metric O
of O
simultaneous O
biogeochemical B-climate-nature
and O
structural O
shifts O
in O
land B-climate-nature
surface I-climate-nature
properties O
which O
we O
interpret O
as O
a O
proxy O
for O
the O
risk O
of O
shifts O
and O
possibly O
disruptions B-climate-impacts
in O
ecosystems O
. O

If O
countries O
fulfil O
their O
current O
emissions B-climate-mitigations
reduction I-climate-mitigations
pledges I-climate-mitigations
, O
resulting O
in O
roughly O
3.5 O
K O
of O
warming O
, O
this O
area O
expands O
to O
cover O
half O
the O
land B-climate-nature
surface I-climate-nature
, O
including O
the O
majority O
of O
tropical B-climate-nature
forests I-climate-nature
and O
savannas B-climate-nature
and O
the O
boreal B-climate-nature
zone I-climate-nature
. O

-DOCSTART- -X- O O664cd3500c45309e381a880e4543ee9a

The O
Swedish O
soil B-climate-nature
water B-climate-nature
model O
SOIL B-climate-models
and O
its O
associated O
nitrogen O
cycle O
model O
SOILN B-climate-models
has O
been O
used O
to O
simulate O
the O
long O
- O
term O
impacts O
( O
over O
12 O
years O
) O
of O
360 O
management O
scenarios O
; O
three O
slurry B-climate-problem-origins
applications O
with O
10 O
spreading O
dates O
( O
involving O
single O
and O
split O
applications O
) O
for O
surface O
spreading O
and O
injection O
of O
slurry B-climate-problem-origins
, O
and O
three O
fertiliser B-climate-problem-origins
applications O
with O
two O
spreading O
dates O
. O

The O
effects O
of O
the O
N O
management O
scenarios O
on O
NO3 O
– O
N O
drainage O
flows O
, O
total O
gaseous B-climate-properties
N I-climate-properties
losses I-climate-properties
and O
crop B-climate-assets
yields I-climate-assets
for O
grass B-climate-assets
, O
winter O
and O
spring O
cereals B-climate-assets
is O
investigated O
. O

Furthermore O
, O
seven O
soils B-climate-nature
with O
varying O
degrees O
of O
drainage O
efficiency O
and O
three O
climatic O
conditions O
( O
East O
and O
West O
coast O
Scotland O
and O
Southern O
Ireland O
) O
are O
studied O
. O

-DOCSTART- -X- O O5e256f4b6e71db2a1f88ea243496ad3d

Climate O
variability O
and O
climate O
change O
have O
negative O
impacts O
on O
fisheries B-climate-assets
ecosystems O
and O
people O
who O
derive O
livelihoods B-climate-assets
from O
them O
. O

Zambian O
climate O
is O
projected O
to O
increase O
2 O
° O
C O
in O
mean B-climate-properties
temperature I-climate-properties
by O
2070 O
, O
and O
further O
reports O
suggest O
that O
rainfall B-climate-nature
will O
drop O
by O
8 O
- O
30 O
% O
of O
the O
normal O
average O
. O

The O
major O
statistical O
techniques O
employed O
in O
this O
research O
include O
estimation O
of O
mean O
frequencies O
and O
correlation O
coefficients O
, O
as O
well O
as O
multivariate O
regression O
analyses O
, O
to O
determine O
the O
relationships O
among O
climate O
( O
temperature B-climate-properties
, O
rainfall B-climate-nature
) O
, O
water B-climate-properties
level I-climate-properties
, O
and O
fish B-climate-assets
yield I-climate-assets
, O
using O
the O
Statistical B-climate-models
Package I-climate-models
for I-climate-models
Social I-climate-models
Sciences I-climate-models
. O

The O
results O
showed O
an O
increase O
in O
temperature B-climate-properties
of O
0.3 O
° O
C O
, O
a O
decrease O
in O
rainfall B-climate-nature
of O
3 O
% O
and O
a O
water B-climate-properties
- I-climate-properties
level I-climate-properties
loss I-climate-properties
of O
1.7 O
m O
since O
1974 O
. O

-DOCSTART- -X- O Oc889a39b89148b5965fb55fdd06589ac

Reindeer B-climate-assets
husbandry I-climate-assets
represents O
a O
major O
land O
use O
in O
the O
Barents O
region O
, O
and O
has O
been O
predicted O
to O
be O
adversely O
affected O
by O
climate O
change O
. O

Key O
natural O
factors O
include O
vegetation B-climate-nature
distribution O
, O
and O
a O
range O
of O
meteorological O
variables O
including O
temperature B-climate-properties
, O
wind B-climate-nature
, O
snow B-climate-nature
cover I-climate-nature
and O
freezing B-climate-nature
of O
rivers B-climate-nature
. O

-DOCSTART- -X- O Oa6f7c2158c8ea705d17f44c18b288e45

Upstream O
range B-climate-hazards
shifts I-climate-hazards
of O
freshwater B-climate-organisms
fishes I-climate-organisms
have O
been O
documented O
in O
recent O
years O
due O
to O
ongoing O
climate O
change O
. O

River B-climate-hazards
fragmentation I-climate-hazards
by O
dams O
, O
presenting O
physical O
barriers O
, O
can O
limit O
the O
climatically O
induced O
spatial B-climate-hazards
redistribution I-climate-hazards
of O
fishes B-climate-organisms
. O

Andean O
freshwater B-climate-nature
ecosystems O
in O
the O
Neotropical O
region O
are O
expected O
to O
be O
highly O
affected O
by O
these O
future O
disturbances O
. O

-DOCSTART- -X- O O29686cc4f254ca30a1c7f4a5e7e486c4

New O
patterns O
of O
drought B-climate-hazards
and O
flooding B-climate-hazards
dealing O
harsh O
blows O
to O
public O
resources O
are O
already O
being O
perceived O
, O
as O
well O
as O
countries O
’ O
social O
and O
economic O
basis O
, O
promoting O
economic O
, O
environmental O
and O
public O
health B-climate-assets
problems O
in O
Latin O
- O
America O
. O

-DOCSTART- -X- O O2b687cb54ef80ad5dbd3e594f88feac3

With O
insights O
from O
historic O
climate O
change O
and O
subsequent O
species B-climate-organisms
’ O
responses O
, O
scientists O
are O
developing O
refined O
tools O
to O
evaluate O
how O
species B-climate-organisms
change O
may O
continue O
in O
the O
future O
and O
what O
impact O
this O
may O
have O
on O
biodiversity B-climate-organisms
and O
conservation O
. O

Furthermore O
, O
bioclimatic O
envelope O
models O
do O
not O
account O
for O
species B-climate-organisms
dispersal B-climate-properties
constraints I-climate-properties
or O
those O
imposed O
by O
disturbances O
such O
as O
land B-climate-problem-origins
use I-climate-problem-origins
change I-climate-problem-origins
or O
fire B-climate-hazards
. O

-DOCSTART- -X- O O150257284552ba26470a84be4a329895

two O
cirrus B-climate-nature
clouds I-climate-nature
with O
different O
optical B-climate-properties
depths I-climate-properties
. O

-DOCSTART- -X- O Ob6e02bdab7a635723f7a77ac1288195c

different O
lidar B-climate-observations
techniques O
are O
responsible O
for O
discrepancies O

-DOCSTART- -X- O Od61a52a615bda46a3542c4e7971b8444

A O
salt O
- O
hydrate O
is O
selected O
in O
this O
case O
, O
based O
on O
its O
nonflammable B-climate-properties
and O
non B-climate-properties
- I-climate-properties
toxic I-climate-properties
character O
, O
high O
latent O
heat B-climate-properties
storage I-climate-properties
and O
high B-climate-properties
thermal I-climate-properties
conductivity I-climate-properties
. O

Comsol B-climate-models
is O
a O
simulation O
platform O
which O
is O
used O
to O
optimize O
physic O
- O
based O
designs O
through O
numerical O
simulations O
. O

-DOCSTART- -X- O O3eae730e9cf1f67fd0ea02d678de5630

We O
review O
relevant O
literature O
on O
the O
contributions O
of O
soil B-climate-nature
microarthropods B-climate-organisms
to O
soil B-climate-nature
health B-climate-assets
through O
their O
intersecting O
roles O
in O
decomposition O
and O
nutrient B-climate-organisms
cycling I-climate-organisms
and O
direct O
and O
indirect O
suppression O
of O
plant B-climate-hazards
pests I-climate-hazards
. O

Microarthropods B-climate-organisms
can O
impact O
soil B-climate-nature
and O
plant B-climate-organisms
health B-climate-assets
directly O
by O
feeding O
on O
pest B-climate-hazards
organisms O
or O
serving O
as O
alternate O
prey O
for O
larger O
predatory O
arthropods B-climate-organisms
. O

-DOCSTART- -X- O Oedeabc82d422c35c2d15f833343adfb0

Following O
the O
9th O
November O
2007 O
east O
coast O
flood B-climate-hazards
event O
, O
a O
group O
of O
collaborators O
within O
the O
FRMRC2 B-climate-organizations
project O
set O
about O
assessing O
and O
improving O
the O
operational O
flood B-climate-hazards
risk O
modelling O
. O

Also O
included O
is O
a O
discussion O
of O
the O
potential O
effect O
of O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
on O
flood B-climate-hazards
risk O
due O
to O
extreme O
events O
. O

It O
is O
likely O
that O
an O
event O
of O
similar O
strength O
to O
the O
2007 O
event O
should O
occur O
approximately O
every O
100 O
years O
. O

However O
, O
with O
a O
0.5 O
metre O
sea B-climate-properties
level I-climate-properties
rise I-climate-properties
, O
such O
an O
event O
would O
be O
likely O
to O
occur O
every O
2 O
years O
. O

-DOCSTART- -X- O O7b3f42c51b7698e294fdfc4d89383e6a

the O
influence O
of O
the O
applied O
lidar B-climate-observations
technique O
plays O
a O
more O
fundamental O
role O

-DOCSTART- -X- O O43cd89ba72ca4233734bd1013a44322b

Model O
results O
showed O
that O
the O
suitable O
distribution B-climate-organisms
range I-climate-organisms
for O
C. B-climate-organisms
minimus I-climate-organisms
is O
western O
and O
southern O
Anatolia O
. O

-DOCSTART- -X- O Oec85783a6bff9512c241f04b4167f0c1

In O
addition O
to O
that O
, O
the O
results O
of O
the O
change O
analysis O
showed O
that O
suitable O
distribution B-climate-organisms
areas I-climate-organisms
for O
the O
species B-climate-organisms
will O
increase O
between O
7 O
% O
and O
13.5 O
% O
with O
time O
. O

-DOCSTART- -X- O O8d2d442050694dc04d63110fb168261d

The O
Three O
- O
River O
Source O
Region O
( O
TRSR O
) O
, O
a O
region O
with O
key O
importance O
to O
the O
ecological B-climate-assets
security I-climate-assets
of O
China O
, O
has O
undergone O
climate O
changes O
and O
a O
shift O
in O
human O
activities O
driven O
by O
a O
series O
of O
ecological B-climate-mitigations
restoration I-climate-mitigations
projects O
in O
recent O
decades O
. O

To O
reveal O
the O
spatiotemporal O
dynamics O
of O
vegetation B-climate-nature
dynamics O
and O
calculate O
the O
contributions O
of O
driving O
factors O
in O
the O
TRSR O
across O
different O
periods O
from O
1982 O
to O
2012 O
, O
net B-climate-properties
primary I-climate-properties
productivity I-climate-properties
( O
NPP B-climate-properties
) O
estimated O
using O
the O
Carnegie B-climate-models
- I-climate-models
Ames I-climate-models
- I-climate-models
Stanford I-climate-models
approach I-climate-models
model O
was O
used O
to O
assess O
the O
status O
of O
vegetation B-climate-nature
. O

Furthermore O
, O
the O
relationships O
of O
NPP B-climate-properties
with O
different O
climate O
factors O
and O
human O
activities O
were O
analyzed O
quantitatively O
. O

-DOCSTART- -X- O Obed9c52a42a05e5410702a358c882bbd

ABSTRACT O
Paddy B-climate-assets
is O
an O
important O
artificial O
wetland B-climate-nature
ecosystem O
related O
to O
sustainable O
development O
of O
agriculture B-climate-assets
and O
environment O
. O

The O
rice B-climate-assets
production O
and O
accompanied O
environmental O
issues O
were O
main O
driving O
factor O
to O
promote O
paddy B-climate-assets
research O
. O

A O
group O
of O
developing O
countries O
led O
by O
China O
and O
India O
played O
important O
role O
in O
global O
paddy B-climate-assets
research O
. O

-DOCSTART- -X- O O799a694748aa68d2232e14e5f499844c

Alternative O
resources O
, O
such O
as O
renewable B-climate-mitigations
energy I-climate-mitigations
sources O
( O
RESs B-climate-mitigations
) O
, O
used O
in O
electricity B-climate-assets
grids I-climate-assets
, O
could O
reduce O
the O
environmental O
impact O
. O

Since O
RESs B-climate-mitigations
are O
inherently O
unreliable O
, O
during O
the O
last O
decades O
the O
scientific O
community O
addressed O
research O
efforts O
to O
their O
integration O
with O
the O
main O
grid O
by O
means O
of O
properly O
designed O
energy O
storage O
systems O
( O
ESSs O
) O
. O

The O
dynamics O
of O
the O
hydrogen O
- O
based O
ESS O
have O
been O
modeled O
by O
means O
of O
the O
mixed O
- O
logic O
dynamic O
( O
MLD O
) O
framework O
in O
order O
to O
capture O
different O
behaviors O
according O
to O
the O
possible O
operating O
modes O
. O

-DOCSTART- -X- O Occd69bbf40f83cb61ce3fa276ff4a6a4

Surface B-climate-nature
runoff I-climate-nature
during O
the O
spring O
will O
be O
higher O
but O
summer O
and O
autumn O
runoff B-climate-nature
can O
be O
slightly O
suppressed O
by O
higher O
transpiration B-climate-properties
of O
deciduous B-climate-organisms
tree I-climate-organisms
species O
. O

Decreased O
summer O
precipitation B-climate-nature
and O
increased O
transpiration B-climate-properties
will O
result O
in O
decreasing O
ground B-climate-nature
water I-climate-nature
discharge B-climate-properties
, O
and O
lowering O
water B-climate-properties
levels I-climate-properties
of O
Volga O
river O
and O
of O
the O
Upper O
Volga O
lakes O
. O

-DOCSTART- -X- O Ocbbe1adbef421d915ee2c2e38d5d5288

Phytoplankton B-climate-organisms
are O
the O
main O
primary B-climate-organisms
producers I-climate-organisms
in O
marine B-climate-nature
ecosystems I-climate-nature
, O
supporting O
important O
food B-climate-organisms
webs I-climate-organisms
. O

They O
are O
recognized O
as O
important O
indicators O
of O
environmental O
changes O
in O
oceans B-climate-nature
and O
coastal B-climate-nature
waters O
. O

Ocean O
colour O
remote O
sensing O
has O
been O
extensively O
used O
to O
study O
phytoplankton B-climate-organisms
throughout O
the O
world O
, O
yet O
there O
is O
still O
much O
to O
understand O
in O
terms O
of O
what O
influences O
phytoplankton B-climate-organisms
variability O
at O
regional O
scales O
. O

Satellite O
chlorophyll B-climate-nature
a I-climate-nature
( O
CHL B-climate-nature
) O
data O
was O
acquired O
from O
the O
Copernicus B-climate-observations
Marine I-climate-observations
environment I-climate-observations
Monitoring I-climate-observations
Service I-climate-observations
( O
CMEMS B-climate-observations
) O
. O

A O
set O
of O
climate O
indices O
, O
satellite O
and O
model O
variables O
were O
then O
used O
as O
environmental O
predictors O
in O
Generalized O
Additive O
Models O
( O
GAMs O
) O
that O
explained O
between O
18.0 O
to O
49.3 O
% O
of O
the O
total O
variance O
of O
CHL B-climate-nature
anomalies O
calculated O
from O
a O
detrended O
and O
deseasoned O
dataset O
. O

In O
the O
northern O
oceanic B-climate-nature
region O
positive O
anomalies O
were O
linked O
to O
high O
North B-climate-nature
Atlantic I-climate-nature
Oscillation I-climate-nature
( O
NAO B-climate-nature
) O
values O
and O
negative O
anomalies O
to O
high O
mixed B-climate-properties
layer I-climate-properties
depths I-climate-properties
. O

-DOCSTART- -X- O Oe0ef394fa4e965c2e96da65b7891fcbc

Use O
of O
exterior B-climate-mitigations
shading I-climate-mitigations
systems O
is O
important O
to O
increase O
energy B-climate-mitigations
savings I-climate-mitigations
in O
residential B-climate-assets
sector O
, O
mainly O
in O
warmer O
climates O
exposed O
to O
direct O
sunlight B-climate-nature
. O

This O
study O
employs O
Life O
Cycle O
Assessment O
( O
LCA O
) O
to O
compare O
the O
effects O
of O
three O
different O
shading B-climate-mitigations
materials O
on O
building B-climate-assets
energy O
consumption O
and O
their O
impacts O
to O
the O
environment O
within O
five O
major O
climate O
zones O
defined O
by O
American B-climate-organizations
Society I-climate-organizations
of I-climate-organizations
Heating I-climate-organizations
, I-climate-organizations
Refrigerating I-climate-organizations
and I-climate-organizations
Air I-climate-organizations
- I-climate-organizations
conditioning I-climate-organizations
Engineers I-climate-organizations
( O
ASHRAE B-climate-organizations
) O
. O

The O
LCA O
framework O
used O
in O
this O
study O
was O
based O
on O
a O
life O
cycle O
methodology O
that O
follows O
the O
International B-climate-organizations
Organization I-climate-organizations
for I-climate-organizations
Standardization I-climate-organizations
( O
ISO B-climate-organizations
) O
14040 B-climate-mitigations
standard I-climate-mitigations
for O
Life O
Cycle O
Assessment O
and O
the O
ASTM B-climate-organizations
standard O
for O
Multi O
- O
Attribute O
Decision O
Analysis O
. O

-DOCSTART- -X- O Oc1254d44285120e679ddcbbc151c5f08

Especially O
in O
the O
Himalayan O
headwaters B-climate-nature
of O
the O
main O
rivers B-climate-nature
in O
South O
Asia O
, O
shifts O
in O
runoff B-climate-nature
are O
expected O
as O
a O
result O
of O
a O
rapidly O
changing O
climate O
. O

In O
recent O
years O
, O
our O
insight O
into O
these O
shifts O
and O
their O
impact O
on O
water B-climate-assets
availability I-climate-assets
has O
increased O
. O

However O
, O
a O
similar O
detailed O
understanding O
of O
the O
seasonal O
pattern O
in O
water B-climate-properties
demand I-climate-properties
is O
surprisingly O
absent O
. O

This O
hampers O
a O
proper O
assessment O
of O
water B-climate-hazards
stress I-climate-hazards
and O
ways O
to O
cope O
and O
adapt O
. O

In O
this O
study O
, O
the O
seasonal O
pattern O
of O
irrigation B-climate-properties
- I-climate-properties
water I-climate-properties
demand I-climate-properties
resulting O
from O
the O
typical O
practice O
of O
multiple O
cropping B-climate-assets
in O
South O
Asia O
was O
accounted O
for O
by O
introducing O
double O
cropping B-climate-assets
with O
monsoon B-climate-nature
- O
dependent O
planting B-climate-mitigations
dates I-climate-mitigations
in O
a O
hydrology B-climate-nature
and O
vegetation B-climate-nature
model O
. O

Crop B-climate-assets
yields I-climate-assets
were O
calibrated O
to O
the O
latest O
state O
- O
level O
statistics O
of O
India O
, O
Pakistan O
, O
Bangladesh O
and O
Nepal O
. O

-DOCSTART- -X- O O0c82ce4a132bf9b4c76f9f0e9bf88240

In O
the O
wake O
of O
the O
climate O
change O
debate O
, O
the O
innovation O
is O
this O
paper O
is O
to O
add O
a O
renewable B-climate-mitigations
energy I-climate-mitigations
source O
to O
the O
Dasgupta B-climate-models
- I-climate-models
Heal I-climate-models
type I-climate-models
of I-climate-models
framework I-climate-models
, O
also O
including O
for O
pollution B-climate-hazards
. O

This O
paper O
presents O
an O
intertemporal O
planning O
horizon O
problem O
in O
the O
presence O
of O
an O
exhaustible B-climate-problem-origins
resource I-climate-problem-origins
, O
a O
renewable B-climate-mitigations
resource I-climate-mitigations
and O
pollution B-climate-hazards
. O

-DOCSTART- -X- O Oda2b85015e48365cf4a765525fca9044

The O
surge O
in O
these O
prices O
has O
been O
attributed O
to O
a O
number O
of O
causes O
, O
including O
the O
production O
of O
first O
generation O
feedstock B-climate-assets
to O
meet O
new O
biofuels B-climate-mitigations
demand O
. O

High O
commodity O
prices O
can O
be O
detrimental B-climate-impacts
for O
households O
’ O
food B-climate-assets
security I-climate-assets
, O
in O
fact O
the O
recent O
price O
surges O
have O
been O
heavily O
discussed O
in O
public O
fora O
and O
have O
been O
a O
serious O
cause O
for O
concern O
in O
developing O
countries O
. O

A O
methodological O
correction O
is O
proposed O
and O
sensitivity O
to O
the O
assumption O
is O
reported O
based O
on O
Peruvian O
and O
Tanzanian O
data O
. O

Conclusions O
on O
welfare B-climate-assets
impacts O
of O
price O
increases O
obtained O
under O
our O
proposed O
methodology O
are O
then O
presented O
for O
the O
case O
of O
Peru O
. O

Our O
results O
suggest O
that O
a O
judgement O
on O
the O
consequences O
of O
bioenergy B-climate-mitigations
production O
on O
food B-climate-assets
security I-climate-assets
should O
be O
made O
case O
by O
case O
. O

-DOCSTART- -X- O O9c79b4f91f4f50444bd724befc61a5ab

the O
last O
years O
of O
university O
it O
was O
born O
in O
me O
the O
clear O
will O
to O
treat O
as O
a O
subject O
of O
the O
final O
thesis O
a O
topic O
related O
to O
disaster B-climate-mitigations
relief I-climate-mitigations
architecture I-climate-mitigations
, O
which O
has O
always O
particularly O
moved O
my O
sensibility O
. O

-DOCSTART- -X- O O7b560d54b7cbcc2b4b0acf15a02fccad

One O
problem O
that O
is O
common O
to O
the O
existing O
research O
on O
future O
climate O
change O
is O
the O
neglect O
of O
the O
forces O
of O
the O
market O
mechanism O
. O

For O
example O
, O
one O
of O
the O
major O
worries O
in O
the O
studies O
on O
climate O
change O
is O
that O
higher O
global B-climate-properties
average I-climate-properties
temperature I-climate-properties
may O
have O
severe O
adverse O
effect O
on O
world O
agricultural B-climate-assets
production I-climate-assets
. O

-DOCSTART- -X- O Od6d877c410735e68b2b959618be0dc52

The O
paper O
consists O
of O
three O
phases O
, O
the O
first O
part O
portrays O
the O
general O
situation O
of O
the O
Philippines O
, O
taking O
into O
analysis O
its O
inherent O
components O
that O
can O
help O
in O
the O
understanding O
of O
the O
problem O
, O
with O
particular O
attention O
to O
the O
study O
of O
the O
climate O
of O
the O
archipelago B-climate-nature
and O
its O
vulnerability O
to O
natural O
disasters O
. O

-DOCSTART- -X- O O4b9089afd2837b7287e579a2f5d960bc

In O
contrast O
, O
soil B-climate-organisms
animals I-climate-organisms
are O
considered O
key O
regulators O
of O
decomposition B-climate-nature
at O
local O
scales O
but O
their O
role O
at O
larger O
scales O
is O
unresolved O
. O

Soil B-climate-organisms
animals I-climate-organisms
are O
consequently O
excluded O
from O
global O
models O
of O
organic B-climate-nature
mineralization I-climate-nature
processes O
. O

-DOCSTART- -X- O O81950f606472cb02c085cfe91093229c

Ice B-climate-nature
clouds I-climate-nature
have O
an O
important O
role O
in O
climate O
. O

They O
are O
strong O
modulators O
of O
the O
outgoing B-climate-properties
longwave I-climate-properties
radiation I-climate-properties
and O
the O
incoming B-climate-properties
shortwave I-climate-properties
radiation I-climate-properties
and O
are O
an O
integral O
part O
of O
the O
hydrological B-climate-nature
cycle I-climate-nature
. O

Climate O
models O
are O
far O
from O
consensus O
on O
the O
magnitude O
and O
spatial O
distribution O
of O
several O
cloud B-climate-nature
parameters O
, O
including O
the O
column O
integrated O
cloud B-climate-nature
ice I-climate-nature
amount O
, O
called O
Ice B-climate-properties
Water I-climate-properties
Path I-climate-properties
( O
IWP B-climate-properties
) O
. O

Cloud B-climate-nature
ice I-climate-nature
retrievals O
from O
satellite O
measurements O
are O
an O
important O
source O
of O
observations O
, O
since O
they O
are O
global O
and O
continuous O
. O

-DOCSTART- -X- O Ob80b2363badf935c82a1dafdf19de744

Boreal B-climate-nature
forest I-climate-nature
fires B-climate-hazards
are O
an O
important O
source O
of O
terrestrial O
carbon O
emissions O
, O
particularly O
during O
years O
of O
widespread O
wildfires B-climate-hazards
. O

Most O
carbon O
emission O
models O
parameterize O
wildfire B-climate-hazards
impacts O
and O
carbon O
flux O
to O
area B-climate-properties
burned I-climate-properties
by O
fires B-climate-hazards
, O
therein O
making O
the O
assumption O
that O
fires B-climate-hazards
consume O
a O
spatiotemporally O
homogeneous O
landscape O
composed O
of O
predominantly O
spruce B-climate-organisms
forests B-climate-nature
and O
peat B-climate-nature
bogs B-climate-nature
with O
deep O
duff B-climate-nature
layers O
. O

We O
examined O
climate O
, O
land B-climate-properties
cover I-climate-properties
, O
area B-climate-properties
burned I-climate-properties
, O
and O
fire B-climate-hazards
impacts O
for O
large O
fires B-climate-hazards
( O
2002 O
to O
2009 O
) O
across O
the O
Alaskan O
boreal B-climate-nature
landscape O
to O
address O
the O
validity O
of O
assumptions O
made O
by O
carbon O
emissions O
models O
for O
boreal B-climate-nature
fires B-climate-hazards
. O

-DOCSTART- -X- O O646fbad716f393fb5c58789b211d2fb2

Australia O
has O
endured O
one O
of O
the O
most O
horrific O
bushfire B-climate-hazards
seasons O
in O
recent O
history O
across O
2019 O
and O
2020 O
, O
with O
devastating O
personal B-climate-impacts
losses I-climate-impacts
and O
environmental B-climate-hazards
impacts I-climate-hazards
. O

Taking O
account O
of O
those O
risks O
is O
certainly O
not O
legally O
prohibited O
by O
the O
shareholder O
primacy O
model O
, O
and O
in O
circumstances O
where O
those O
risks O
intersect O
with O
the O
business B-climate-assets
of O
the O
company O
, O
failing O
to O
take O
those O
risks O
into O
account O
would O
likely O
be O
a O
breach O
of O
duty O
. O

-DOCSTART- -X- O Oa772e12e30b0495e893ebd4485c3f088

In O
order O
to O
evaluate O
drought B-climate-hazards
risk O
at O
upland B-climate-nature
according O
to O
climate O
change O
scenario O
( O
RCP8.5 B-climate-datasets
) O
, O
we O
have O
carried O
out O
the O
simulation O
using O
agricultural O
water B-climate-nature
balance I-climate-nature
estimation O
model O
, O
called O
AFKAE0.5 B-climate-models
, O
at O
66 O
weather O
station O
sites O
in O
2020 O
, O
2046 O
, O
2050 O
, O
2084 O
, O
and O
2090 O
. O

Total B-climate-properties
Drought I-climate-properties
Risk I-climate-properties
Index I-climate-properties
between O
the O
first O
month O
( O
f O
) O
and O
last O
month O
( O
l O
) O
( O
TDRI(f B-climate-properties
/ I-climate-properties
l I-climate-properties
) O
) O
and O
maximum B-climate-properties
continuous I-climate-properties
drought I-climate-properties
risk I-climate-properties
index I-climate-properties
( O
MCDRI(f B-climate-properties
/ I-climate-properties
l I-climate-properties
) O
) O
were O
defined O
as O
the O
index O
for O
analyzing O
pattern O
and O
strength O
of O
drought B-climate-hazards
simulated O
by O
the O
model O
. O

-DOCSTART- -X- O Oc38569cd6a3d870dd662e777c2222d8b

Soil B-climate-nature
organic I-climate-nature
matter I-climate-nature
is O
a O
vast O
store O
of O
carbon O
, O
with O
a O
critical O
role O
in O
the O
global O
carbon B-climate-nature
cycle I-climate-nature
. O

Despite O
its O
importance O
, O
the O
dynamics O
of O
soil B-climate-nature
organic I-climate-nature
carbon I-climate-nature
decomposition B-climate-nature
, O
under O
the O
impact O
of O
climate O
change O
or O
changing O
litter B-climate-nature
inputs O
, O
are O
poorly O
understood O
. O

Current O
biogeochemical B-climate-nature
models O
usually O
lack O
microbial B-climate-organisms
processes O
and O
thus O
miss O
an O
important O
feedback O
when O
considering O
the O
fate O
of O
carbon O
. O

-DOCSTART- -X- O O9ac59758cb965ab00aa64d03b7406773

West O
Coast O
sablefish B-climate-organisms
are O
economically O
valuable O
, O
with O
landings O
of O
11.8 O
million O
pounds O
valued O
at O
over O
$ O
31 O
million O
during O
2016 O
, O
making O
assessing O
and O
understanding O
the O
impact O
of O
climate O
change O
on O
the O
California O
Current O
( O
CC O
) O
stock O
a O
priority O
for O
( O
1 O
) O
forecasting O
future O
stock O
productivity O
, O
and O
( O
2 O
) O
testing O
the O
robustness O
of O
management O
strategies O
to O
climate O
impacts O
. O

Sablefish B-climate-organisms
recruitment O
is O
related O
to O
large O
- O
scale O
climate O
forcing O
indexed O
by O
regionally O
correlated O
sea B-climate-properties
level I-climate-properties
( O
SL B-climate-properties
) O
and O
zooplankton B-climate-organisms
communities O
that O
pelagic B-climate-nature
young O
- O
of O
- O
the O
- O
year O
sablefish B-climate-organisms
feed O
upon O
. O

-DOCSTART- -X- O Ob63df54fc00da0b275b9f1dce3490162

Landuse B-climate-problem-origins
change I-climate-problem-origins
significantly O
alters O
the O
hydrologic B-climate-nature
characteristics O
of O
the O
land B-climate-nature
surface I-climate-nature
within O
a O
watershed B-climate-nature
. O

The O
watershed B-climate-nature
has O
seven O
major O
landuse O
classes O
, O
namely O
agriculture B-climate-assets
, O
built B-climate-assets
- I-climate-assets
up I-climate-assets
, O
fallow B-climate-assets
land I-climate-assets
, O
forest B-climate-nature
, O
grass B-climate-nature
land I-climate-nature
, O
streams B-climate-nature
, O
and O
water B-climate-nature
bodies I-climate-nature
. O

Forest B-climate-nature
is O
the O
most O
affected O
landuse O
among O
all O
watershed B-climate-nature
landuses O
that O
shrinked O
by O
194.90 O
ha O
followed O
by O
agriculture B-climate-assets
( O
64.57 O
ha O
) O
, O
grass B-climate-nature
land I-climate-nature
( O
50.81 O
ha O
) O
, O
streams B-climate-nature
( O
30.42 O
ha O
) O
, O
fallow B-climate-assets
land I-climate-assets
( O
21.86 O
ha O
) O
, O
and O
water B-climate-nature
bodies I-climate-nature
( O
9.72 O
ha O
) O
. O

Runoff B-climate-nature
and O
sediment B-climate-properties
yield I-climate-properties
for O
the O
landuse O
of O
the O
years O
2006 O
and O
2016 O
were O
simulated O
by O
the O
WEPP B-climate-models
model O
using O
two O
climate O
scenarios O
( O
2006 O
and O
2016 O
) O
. O

-DOCSTART- -X- O Oc9a3a56f744a069d300fc98febc470ad

Particularly O
, O
as O
the O
significant O
changes O
of O
agriculture B-climate-assets
land O
and O
forest B-climate-nature
, O
typical O
characteristics O
of O
pattern O
and O
process O
of O
agroforestry B-climate-mitigations
ecotone B-climate-nature
change O
formed O
in O
recent O
decades O
. O

This O
paper O
took O
agroforestry B-climate-mitigations
ecotone B-climate-nature
of O
Nenjiang O
River O
Basin O
in O
China O
as O
study O
region O
and O
simulated O
temperature B-climate-properties
change O
based O
on O
land B-climate-properties
cover I-climate-properties
change O
from O
1950s O
to O
1978 O
and O
from O
1978 O
to O
2010 O
. O

The O
analysis O
of O
temperature B-climate-properties
difference I-climate-properties
sensitivity I-climate-properties
to O
land B-climate-problem-origins
cover I-climate-problem-origins
change I-climate-problem-origins
based O
on O
Weather B-climate-models
Research I-climate-models
and I-climate-models
Forecasting I-climate-models
( O
WRF B-climate-models
) O
model O
showed O
that O
the O
land B-climate-problem-origins
cover I-climate-problem-origins
change I-climate-problem-origins
from O
1950s O
to I-climate-properties
1978 I-climate-properties
induced O
warming B-climate-properties
effect I-climate-properties
over O
all O
the O
study O
area O
, O
including O
the O
change O
of O
grassland B-climate-nature
to O
agriculture B-climate-assets
land O
, O
grassland B-climate-nature
to O
deciduous B-climate-organisms
broad B-climate-organisms
- I-climate-organisms
leaved I-climate-organisms
forest B-climate-nature
, O
and O
deciduous B-climate-organisms
broad B-climate-organisms
- I-climate-organisms
leaved I-climate-organisms
forest B-climate-nature
to O
shrub B-climate-nature
land I-climate-nature
. O

-DOCSTART- -X- O Oebd017852b95a3f620496f057bf11ba0

Dynamic O
vegetation O
models O
( O
DVMs O
) O
used O
to O
predict O
forest B-climate-nature
dynamics O
are O
typically O
based O
on O
simple O
, O
largely O
data O
- O
free O
( O
‘ O
theoretical O
’ O
) O
mortality O
algorithms O
( O
MAs O
) O
. O

A O
systematic O
comparison O
of O
eight O
MAs O
( O
seven O
inventory O
- O
based O
, O
one O
‘ O
theoretical O
’ O
) O
for O
the O
pan O
- O
European O
tree B-climate-organisms
species B-climate-organisms
Pinus B-climate-organisms
sylvestris I-climate-organisms
L. I-climate-organisms
was O
carried O
out O
within O
the O
DVM O
ForClim B-climate-models
for O
present O
and O
future O
climate O
scenarios O
at O
three O
contrasting O
sites O
across O
Europe O
. O

-DOCSTART- -X- O O24c0978c0eeb0c9008e69729628bcdf5

Monthly B-climate-properties
mean I-climate-properties
maximum I-climate-properties
and I-climate-properties
minimum I-climate-properties
temperatures I-climate-properties
, O
monthly B-climate-properties
total I-climate-properties
rainfall I-climate-properties
and O
monthly B-climate-properties
mean I-climate-properties
relative I-climate-properties
humidity I-climate-properties
were O
positively O
correlated O
to O
monthly O
notification O
of O
Japanese B-climate-impacts
encephalitis I-climate-impacts
, O
while O
monthly B-climate-properties
mean I-climate-properties
air I-climate-properties
pressure I-climate-properties
was O
inversely O
correlated O
. O

Thresholds O
of O
25.2 O
degrees O
C O
for O
maximum B-climate-properties
temperature I-climate-properties
and O
21.0 O
degrees O
C O
for O
minimum B-climate-properties
temperature I-climate-properties
were O
also O
detected O
. O

-DOCSTART- -X- O Oad3eb8b149eb30a7961d5fc10f22b09d

In O
order O
to O
address O
carbon B-climate-problem-origins
leakage I-climate-problem-origins
and O
preserve O
the O
competitiveness O
of O
domestic O
industries O
, O
some O
industrialized O
Annex O
I O
countries O
have O
proposed O
to O
implement O
carbon B-climate-mitigations
tariffs I-climate-mitigations
. O

This O
proposal O
is O
evaluated O
using O
an O
energy O
- O
economic O
model O
of O
the O
global O
economy O
. O

The O
results O
indicate O
that O
carbon B-climate-mitigations
tariffs I-climate-mitigations
could O
raise O
US$ O
3.5–24.5 O
billion O
( O
with O
a O
central O
value O
of O
$ O
9.8 O
billion O
) O
for O
clean O
development O
financing O
. O

-DOCSTART- -X- O Odefc49b0f62c621e2c74d7649e1d52bb

Abstract O
Long O
- O
term O
changes O
in O
wave B-climate-nature
climate O
have O
potential O
impacts O
on O
the O
evolution O
of O
regional O
coastlines B-climate-nature
. O

This O
study O
investigates O
the O
impact O
of O
variable O
wave O
climate O
on O
the O
temporal O
dynamics O
of O
longshore B-climate-nature
sediment I-climate-nature
transport I-climate-nature
( O
LST B-climate-nature
) O
, O
which O
plays O
a O
major O
role O
in O
defining O
the O
overall O
coastal B-climate-nature
geomorphology O
of O
regional O
coastlines B-climate-nature
. O

-DOCSTART- -X- O Obc58fc1c76ce0b9dc963cebd440bebc8

and O
decrease O
in O
surface B-climate-properties
temperature I-climate-properties
by O
− O
1 O
∘ O
C. O

-DOCSTART- -X- O Oe98b67dc32c17b385d7dacc54d40f45a

Increasing O
the O
LCC B-climate-assets
share O
from O
28 O
% O
to O
38 O
% O
in O
the O
Kraichgau O
region O
led O
to O
a O
decrease O
in O
latent B-climate-properties
heat I-climate-properties
flux I-climate-properties
( O
LE B-climate-properties
) O
. O

-DOCSTART- -X- O O8ea20119b1b47c19b3529e81d2566fe2

Some O
losses O
of O
N O
to O
the O
atmosphere B-climate-nature
are O
unavoidable O
, O
at O
least O
35 O
% O
of O
excreted B-climate-problem-origins
N I-climate-problem-origins
in O
best O
case O
scenarios O
and O
60 O
% O
, O
or O
more O
, O
in O
most O
situations O
. O

To O
achieve O
environmentally O
acceptable O
nutrient O
balances O
, O
many O
animal B-climate-assets
production I-climate-assets
facilities O
will O
have O
to O
export O
manure B-climate-problem-origins
or O
manure B-climate-problem-origins
products O
or O
manipulate O
nutrient O
production O
to O
match O
nutrient O
needs O
. O

-DOCSTART- -X- O O24677988fe867046e95a337086e5d5b0

Present O
- O
day O
flood B-climate-hazards
estimation O
practise O
is O
underpinned O
by O
the O
assumption O
that O
flood B-climate-hazards
risk O
in O
a O
future O
climate O
will O
reflect O
historical O
flood B-climate-hazards
risk O
as O
represented O
by O
the O
instrumental O
record O
. O

This O
assumption O
, O
which O
is O
commonly O
referred O
to O
as O
the O
assumption O
of O
stationarity O
, O
recently O
has O
been O
questioned O
as O
a O
result O
of O
both O
an O
increased O
appreciation O
of O
the O
natural O
variability O
in O
our O
hydroclimate B-climate-nature
at O
temporal O
scales O
beyond O
that O
of O
the O
instrumental O
record O
, O
as O
well O
as O
the O
projected O
intensification O
of O
the O
hydrologic B-climate-nature
cycle O
due O
to O
anthropogenic O
climate O
change O
. O

These O
developments O
have O
led O
some O
authors O
to O
suggest O
that O
the O
stationarity O
assumption O
should O
henceforth O
be O
considered O
invalid O
, O
thereby O
calling O
into O
question O
all O
the O
methods O
that O
are O
underpinned O
by O
it O
, O
including O
flood B-climate-hazards
frequency O
analysis O
using O
observed O
streamflow O
records O
, O
and O
rainfall B-climate-properties
- I-climate-properties
runoff I-climate-properties
modelling O
informed O
by O
instrumental O
precipitation B-climate-nature
and O
streamflow B-climate-nature
records O

-DOCSTART- -X- O Ob77d5d6a680f4e9545a5280b9f640398

In O
Morocco O
, O
cattle B-climate-assets
production O
is O
one O
of O
the O
most O
important O
components O
of O
the O
agricultural B-climate-assets
economy O
; O
a O
sector O
which O
contributes O
heavily O
to O
the O
development O
of O
the O
Moroccan O
economy O
. O

The O
development O
of O
this O
sector O
is O
faced O
by O
many O
problems O
, O
like O
poor O
infrastructure B-climate-assets
, O
lack O
of O
services O
and O
climate O
change O
, O
in O
addition O
to O
infectious O
diseases B-climate-impacts
like O
bovine B-climate-impacts
tuberculosis I-climate-impacts

-DOCSTART- -X- O O35ed5f3f46657f3c809884fef3967da5

Under O
the O
impacts O
of O
climate O
variability O
and O
human O
activities O
, O
there O
are O
statistically O
significant O
decreasing O
trends O
for O
streamflow B-climate-properties
in O
the O
Yellow O
River O
basin O
, O
China O
. O

Therefore O
, O
it O
is O
crucial O
to O
separate O
the O
impacts O
of O
climate O
variability O
and O
human O
activities O
on O
streamflow B-climate-properties
decrease O
for O
better O
water B-climate-mitigations
resources I-climate-mitigations
planning I-climate-mitigations
and I-climate-mitigations
management I-climate-mitigations
. O

In O
this O
study O
, O
the O
Qinhe O
River O
basin O
( O
QRB O
) O
, O
a O
typical O
sub B-climate-nature
- I-climate-nature
basin I-climate-nature
in O
the O
middle O
reach O
of O
the O
Yellow O
River O
, O
was O
chosen O
as O
the O
study O
area O
to O
assess O
the O
impacts O
of O
climate O
variability O
and O
human O
activities O
on O
streamflow B-climate-properties
decrease O
. O

The O
trend O
and O
breakpoint O
of O
observed O
annual B-climate-properties
streamflow I-climate-properties
from O
1956 O
to O
2010 O
were O
identified O
by O
the O
nonparametric O
Mann O
– O
Kendall O
test O
. O

The O
observed O
annual B-climate-properties
streamflow I-climate-properties
decreased O
by O
68.1 O
mm O
from O
102.3 O
to O
34.2 O
mm O
in O
the O
two O
periods O
. O

-DOCSTART- -X- O O61d999271f30da6b2ebfccdfd43b267d

Floods B-climate-hazards
, O
landslides B-climate-hazards
, O
and O
mudslides B-climate-hazards
are O
frequent O
phenomena O
in O
Rio O
de O
Janeiro O
state O
( O
RJ O
) O
. O

In O
the O
past O
decades O
, O
several O
catastrophic O
events O
have O
occurred O
and O
caused O
severe O
damages B-climate-impacts
to O
people B-climate-assets
and O
infrastructure B-climate-assets
. O

In O
contrast O
, O
the O
persistent O
droughts B-climate-hazards
that O
affected O
Southeast O
Brazil O
between O
2014 O
and O
2017 O
are O
phenomena O
that O
were O
not O
known O
earlier O
– O
at O
least O
in O
such O
frequency B-climate-properties
and O
intensity B-climate-properties
. O

Furthermore O
, O
we O
reconstruct O
in O
how O
far O
climate O
variability O
and O
human O
impact O
( O
in O
particular O
deforestation B-climate-problem-origins
) O
affected O
the O
occurrence O
of O
hydrometeorological B-climate-hazards
hazards I-climate-hazards
in O
the O
Holocene O
. O

-DOCSTART- -X- O O8b2c56dc6217307073ea5d0ae00647e4

Analyzing O
the O
critical O
case O
of O
Copenhagen O
in O
a O
degrowth B-climate-mitigations
perspective O
, O
sheds O
doubts O
on O
sustainable B-climate-mitigations
urban I-climate-mitigations
development I-climate-mitigations
, O
but O
does O
not O
imply O
the O
rejection O
of O
all O
its O
typical O
planning O
measures O
. O

-DOCSTART- -X- O Obdf2739f88cff3cdc1e7ce3502f915e4

In O
this O
study O
the O
distributed O
, O
process O
- O
oriented O
, O
physically O
- O
based O
water B-climate-nature
balance I-climate-nature
model O
MODBIL B-climate-models
is O
set O
up O
for O
two O
Kalahari O
sub O
- O
catchments O
in O
northeastern O
Namibia O
and O
northwestern O
Botswana O
to O
show O
possible O
impacts O
of O
climatic O
change O
on O
groundwater B-climate-properties
recharge I-climate-properties
. O

The O
results O
of O
the O
22 O
- O
year O
- O
long O
calibration O
period O
are O
verified O
with O
the O
chloride O
mass B-climate-properties
balance I-climate-properties
and O
hydrographs O
, O
and O
are O
in O
the O
next O
step O
compared O
with O
results O
of O
a O
prediction O
simulation O
. O

With O
this O
approach O
an O
increase O
in O
mean O
groundwater B-climate-properties
recharge I-climate-properties
and O
interflow B-climate-nature
is O
predicted O
for O
the O
two O
catchments B-climate-nature
. O

-DOCSTART- -X- O Of811b7acd89b1a50667103e8055c7e42

In O
recent O
years O
, O
natural O
hazards O
involving O
large O
mass O
movements O
such O
as O
landslides B-climate-hazards
, O
debris B-climate-hazards
flows I-climate-hazards
, O
and O
mudslides B-climate-hazards
have O
significantly O
increased O
in O
frequency B-climate-properties
due O
to O
the O
influence O
of O
global O
warming O
and O
climate O
change O
. O

These O
phenomena O
often O
carry O
huge O
rocks B-climate-nature
and O
heavy O
materials O
that O
may O
, O
directly O
or O
indirectly O
, O
cause O
damage B-climate-impacts
to O
structures O
and O
the O
landscape O
. O

While O
the O
current O
state O
of O
the O
art O
of O
landslide B-climate-hazards
prediction O
using O
numerical O
methods O
has O
been O
mainly O
dominated O
by O
the O
development O
of O
advance O
geomechanical B-climate-nature
models O
suited O
for O
different O
types O
of O
soil B-climate-nature
materials O
, O
e.g. O
multi O
- O
phase O
unsaturated B-climate-nature
soil I-climate-nature
model O
, O
this O
study O
focuses O
more O
on O
the O
interaction O
of O
such O
phenomena O
with O
the O
installed O
protective B-climate-mitigations
structures I-climate-mitigations
. O

Here O
, O
an O
implicit O
formulation O
of O
material O
point O
method O
( O
MPM O
) O
is O
implemented O
to O
model O
the O
landslides B-climate-hazards
considering O
finite O
strain O
assumption O
. O

Furthermore O
, O
a O
staggered O
coupling O
scheme O
with O
the O
Finite O
Element O
Method O
is O
proposed O
to O
simulate O
accurately O
and O
robustly O
the O
dynamic B-climate-properties
force I-climate-properties
and O
displacement O
coupling O
of O
soil O
- O
structure O
interaction O
. O

-DOCSTART- -X- O O415912faeabe3c1bff5820ea3ea867fd

We O
use O
a O
random O
coefficients O
modeling O
approach O
to O
examine O
U.S. O
relationships O
between O
the O
intensity O
of O
impervious B-climate-problem-origins
surface I-climate-problem-origins
within O
a O
county O
, O
population B-climate-properties
density I-climate-properties
in O
impervious B-climate-problem-origins
areas I-climate-problem-origins
, O
and O
carbon B-climate-properties
intensity I-climate-properties
of O
well O
- O
being O
( O
CIWB)—here O
constructed O
using O
industrial O
emissions O
. O

-DOCSTART- -X- O O3122494401bc5a9cddc54efa71e43de2

The O
disasters O
include O
Cyclone O
Nargis O
in O
Burma O
; O
earthquakes B-climate-hazards
in O
China O
, O
India O
, O
Pakistan O
, O
Iran O
, O
Turkey O
, O
and O
Greece O
; O
Hurricane O
Katrina O
in O
the O
United O
States O
; O
the O
tsunami B-climate-hazards
in O
Aceh O
, O
Sri O
Lanka O
, O
and O
other O
territories O
; O
drought B-climate-hazards
in O
Ethiopia O
, O
southern O
Africa O
, O
and O
Cuba O
; O
famine B-climate-impacts
in O
North O
Korea O
; O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
in O
islands B-climate-nature
; O
disasters O
in O
the O
Philippines O
; O
and O
vaccination O
and O
casualty O
identification O
. O

Among O
multiple O
pathways O
of O
disaster B-climate-mitigations
diplomacy I-climate-mitigations
leading O
to O
success O
or O
failure O
, O
four O
broad O
pathways O
emerge O
. O

-DOCSTART- -X- O Oe7512aae3d62c34d51074478020e88a9

We O
test O
this O
indicator O
using O
Lepidopteran B-climate-organisms
and O
co O
- O
located O
weather O
data O
collected O
across O
a O
range O
of O
UK B-climate-organizations
Environmental I-climate-organizations
Change I-climate-organizations
Network I-climate-organizations
( O
ECN B-climate-organizations
) O
sites O
. O

We O
compare O
our O
butterfly B-climate-organisms
indicator O
with O
estimates O
derived O
from O
an O
alternative O
, O
previously O
published O
metric O
, O
the O
Community B-climate-properties
Temperature I-climate-properties
Index I-climate-properties
( O
CTI B-climate-properties
) O
. O

-DOCSTART- -X- O Oe65c892f4ba633ffb07eef17c85c2b99

site O
CTR B-climate-properties
was O
positively O
correlated O
with O
mean B-climate-properties
site I-climate-properties
temperature I-climate-properties
for O
moths B-climate-organisms
but O
not O
butterflies B-climate-organisms
. O

-DOCSTART- -X- O Od9aecfafe766bebf7c7f20731311a5ab

Oil B-climate-assets
- I-climate-assets
and I-climate-assets
- I-climate-assets
gas I-climate-assets
networks I-climate-assets
are O
systems O
of O
pumps B-climate-assets
and O
pipelines B-climate-assets
that O
are O
exposed O
to O
heterogeneous O
threats O
. O

-DOCSTART- -X- O O083e1ddd03178a11a776c0082f827464

Results O
from O
a O
new O
version O
of O
the O
Hadley B-climate-models
Centre I-climate-models
atmospheric I-climate-models
general I-climate-models
- I-climate-models
circulation I-climate-models
model I-climate-models
are O
presented O
in O
which O
the O
sensitivity O
of O
the O
model O
to O
the O
inclusion O
of O
a O
simple O
aerosol B-climate-nature
climatology O
is O
investigated O
. O

Without O
aerosols B-climate-nature
, O
comparisons O
with O
clear B-climate-properties
- I-climate-properties
sky I-climate-properties
reflected I-climate-properties
short I-climate-properties
- I-climate-properties
wave I-climate-properties
radiation I-climate-properties
from O
the O
Earth B-climate-observations
Radiation I-climate-observations
Budget I-climate-observations
Experiment I-climate-observations
( O
ERBE B-climate-observations
) O
and O
the O
Scanner B-climate-observations
for I-climate-observations
Radiation I-climate-observations
Budget I-climate-observations
( O
ScaRaB B-climate-observations
) O
satellite O
measurements O
indicate O
a O
missing O
scatterer B-climate-nature
in O
the O
clear B-climate-nature
- I-climate-nature
sky I-climate-nature
atmosphere B-climate-nature
. O

In O
addition O
, O
recent O
estimates O
of O
global O
, O
annual O
mean O
absorbed O
short B-climate-nature
- I-climate-nature
wave I-climate-nature
radiation I-climate-nature
at O
the O
surface O
, O
which O
used O
the O
Global B-climate-datasets
Energy I-climate-datasets
Balance I-climate-datasets
Archive I-climate-datasets
( O
GEBA B-climate-datasets
) O
where O
possible O
for O
verification O
, O
indicate O
that O
the O
atmosphere B-climate-nature
did O
not O
absorb O
enough O
short B-climate-nature
- I-climate-nature
wave I-climate-nature
radiation I-climate-nature
. O

The O
insertion O
of O
a O
simple O
aerosol B-climate-nature
climatology O
based O
on O
World B-climate-organizations
Climate I-climate-organizations
Research I-climate-organizations
Programme I-climate-organizations
recommendations O
into O
the O
model O
was O
found O
to O
be O
the O
only O
plausible O
means O
of O
reducing O
its O
significant O
short B-climate-nature
- I-climate-nature
wave I-climate-nature
bias O
compared O
with O
ERBE B-climate-observations
and O
ScaRaB B-climate-observations
measurements O
at O
the O
top O
of O
the O
atmosphere B-climate-nature
, O
and O
with O
GEBA B-climate-datasets
data O
at O
the O
surface O
. O

-DOCSTART- -X- O O27fa447fa7376e8a47748f68cf654093

Using O
NDVI B-climate-datasets
data O
from O
1982 O
to O
2015 O
, O
this O
study O
investigated O
the O
spatiotemporal O
change O
of O
spring O
green B-climate-properties
- I-climate-properties
up I-climate-properties
date I-climate-properties
( O
GUD B-climate-properties
) O
across O
the O
Yellow O
River O
Basin O
( O
YRB O
) O
and O
estimated O
the O
possible O
effects O
of O
different O
climatic O
factors O
on O
it O
. O

-DOCSTART- -X- O O0a8a3872322b14f3b03cd2be62786f79

Using O
field O
investigations O
, O
Geographical O
Information O
System O
( O
GIS O
) O
analyses O
, O
and O
mathematical O
and O
conceptual O
models O
, O
we O
reveals O
how O
anthropogenic O
activity O
influences O
processes O
at O
multiple O
time O
and O
space O
scales O
, O
with O
enduring O
effects O
. O

Using O
the O
earthquake B-climate-hazards
- O
induced O
land B-climate-hazards
- I-climate-hazards
subsidence I-climate-hazards
experienced O
in O
Christchurch O
, O
New O
Zealand O
, O
as O
a O
relative O
SLR B-climate-hazards
example O
( O
‘ O
Laboratory O
Christchurch O
’ O
) O
, O
evidence O
shows O
that O
coastal B-climate-nature
settlements O
are O
likely O
to O
be O
impacted O
not O
only O
at O
the O
shore B-climate-nature
but O
further O
inland O
via O
coast- B-climate-nature
connected I-climate-nature
waterways I-climate-nature
, O
where O
drainage B-climate-nature
is O
impeded O
due O
to O
an O
increase O
in O
the O
base O
level O
of O
that O
is O
the O
sea O
. O

-DOCSTART- -X- O Oa88360284cf7e93221a7757744e5f294

Increasing O
demographic B-climate-problem-origins
pressure I-climate-problem-origins
, O
economic O
development O
and O
resettlement O
policies O
in O
the O
Lower O
Mekong O
Basin O
induce O
greater O
population O
dependency O
on O
river B-climate-nature
flow O
to O
satisfy O
growing O
domestic O
and O
agricultural B-climate-assets
water B-climate-properties
demands I-climate-properties
. O

-DOCSTART- -X- O Obb7ab1fb437a59c7f22b4a5d5f562be3

In O
this O
study O
relative O
ecological O
risk O
assessment O
was O
done O
for O
future O
potential O
introductions O
of O
three O
species B-climate-organisms
in O
the O
Canadian O
Arctic O
: O
periwinkle B-climate-organisms
Littorina B-climate-organisms
littorea I-climate-organisms
, O
soft B-climate-organisms
shell I-climate-organisms
clam I-climate-organisms
Mya B-climate-organisms
arenaria I-climate-organisms
and O
red B-climate-organisms
king I-climate-organisms
crab I-climate-organisms
Paralithodes B-climate-organisms
camtschaticus I-climate-organisms
. O

The O
main O
ports O
of O
Deception O
Bay O
and O
Churchill O
were O
classified O
as O
being O
at O
moderate O
to O
high O
relative O
risk O
for O
L. B-climate-organisms
littorea I-climate-organisms
and O
M. B-climate-organisms
arenaria I-climate-organisms
, O
especially O
from O
domestic O
vessels O
, O
while O
relative O
overall O
risk O
for O
P. B-climate-organisms
camtschaticus I-climate-organisms
was O
low O
for O
international O
vessels O
and O
null O
for O
domestic O
vessels O
due O
to O
few O
ships O
transiting O
from O
its O
range O
of O
distribution O
to O
Canadian O
Arctic O
ports O
. O

-DOCSTART- -X- O O3cbad3fa661b831573d10d1b5f7a0ed1

This O
paper O
presents O
SAT B-climate-mitigations
( O
the O
Spanish O
acronym O
for O
Early B-climate-mitigations
Warning I-climate-mitigations
System I-climate-mitigations
) O
, O
a O
decision O
support O
system O
( O
DSS O
) O
, O
for O
assessing O
the O
risk O
of O
desertification B-climate-hazards
in O
Spain O
, O
where O
20 O
% O
of O
the O
land O
has O
already O
been O
desertified B-climate-hazards
and O
1 O
% O
is O
in O
active O
degradation O
. O

SAT B-climate-mitigations
relies O
on O
three O
versions O
of O
a O
Generic B-climate-models
Desertification I-climate-models
Model I-climate-models
( O
GDM B-climate-models
) O
that O
integrates O
economics O
and O
ecology O
under O
the O
predator O
- O
prey O
paradigm O
. O

Given O
the O
general O
nature O
of O
the O
tool O
and O
the O
fact O
that O
all O
United B-climate-mitigations
Nations I-climate-mitigations
Convention I-climate-mitigations
to I-climate-mitigations
Combat I-climate-mitigations
Desertification I-climate-mitigations
( O
UNCCD B-climate-mitigations
) O
signatory O
countries O
are O
committed O
to O
developing O
their O
National B-climate-mitigations
Plans I-climate-mitigations
to I-climate-mitigations
Combat I-climate-mitigations
Desertification I-climate-mitigations
( O
NPCD B-climate-mitigations
) O
, O
SAT B-climate-mitigations
could O
be O
exported O
to O
regions O
threatened O
by O
desertification B-climate-hazards
and O
expanded O
to O
cover O
more O
case O
studies O
. O

-DOCSTART- -X- O O802611dcabb20407785f557361cfb760

To O
later O
analyse O
and O
plan O
European O
GHG O
mitigation B-climate-mitigations
scenario O
for O
the O
livestock B-climate-assets
sectors O
, O
a O
particular O
effort O
of O
description O
of O
the O
Livestock B-climate-assets
Production O
Systems O
( O
LPS O
) O
in O
place O
in O
Europe O
is O
necessary O
, O
livestock B-climate-assets
production O
differing O
largely O
over O
Europe O
. O

For O
that O
, O
Work O
Package O
2 O
( O
WP2 O
) O
of O
the O
GGELS B-climate-organizations
project O
has O
to O
focus O
on O
the O
conceptualisation O
and O
build O
up O
of O
a O
new O
LPS O
typology O
allowing O
policy O
makers O
to O
precisely O
identify O
LPS O
diversity O
. O

-DOCSTART- -X- O O025a6e490e1d3eb64effc682f5c00cb9

The O
current O
work O
compares O
the O
drivers O
of O
hydrological B-climate-nature
flux O
changes O
and O
flood B-climate-hazards
reoccurrences O
in O
the O
Lijiang O
River O
basin O
. O

Using O
satellite O
images O
, O
SWAT B-climate-models
and O
HEC B-climate-models
- I-climate-models
HMS I-climate-models
models O
, O
Mann O
- O
Kendall O
and O
Minitab O
trend O
tests O
, O
analysis O
on O
climatic O
as O
well O
as O
landuse B-climate-problem-origins
/ I-climate-problem-origins
land I-climate-problem-origins
cover I-climate-problem-origins
changes I-climate-problem-origins
( O
LULCC B-climate-problem-origins
) O
were O
carried O
out O
statistically O
. O

Landuse O
maps O
were O
used O
to O
observe O
the O
watershed B-climate-nature
land O
conversion O
during O
pre O
/ O
post O
- O
development O
via O
GIS O
. O

HEC B-climate-models
- I-climate-models
HMS I-climate-models
model O
results O
revealed O
negligible O
changes O
of O
hydrographs O
with O
and O
without O
LULCC B-climate-problem-origins
. O

-DOCSTART- -X- O Oe367dc41006c3a99f0b03c16116d9645

-DOCSTART- -X- O O3010e028fce442543990bacf933a6b08

Reconstruction O
of O
interannual O
variability O
of O
net B-climate-properties
ecosystem I-climate-properties
productivity I-climate-properties
( O
NEP B-climate-properties
) O
in O
forests B-climate-nature
provides O
an O
important O
approach O
to O
analyse O
impacts O
of O
future O
climate O
change O
on O
global O
carbon O
( O
C O
) O
cycling B-climate-mitigations
. O

In O
this O
study O
, O
annual O
NEP B-climate-properties
at O
12 O
Fluxnet B-climate-observations
- O
Canada O
forest B-climate-nature
sites O
( O
93 O
site O
- O
year O
) O
was O
simulated O
using O
a O
process O
- O
based O
Integrated B-climate-models
Terrestrial I-climate-models
Ecosystem I-climate-models
C I-climate-models
- I-climate-models
budget I-climate-models
( O
InTEC B-climate-models
) O
model O
driven O
by O
forest B-climate-nature
inventory O
data O
, O
site O
- O
level O
meteorological O
measurements O
, O
site O
- O
specific O
indicators O
, O
and O
remote O
sensing O
observations O
. O

Our O
results O
indicate O
that O
the O
InTEC B-climate-models
model O
can O
capture O
the O
first O
order O
of O
interannual O
NEP B-climate-properties
variability O
with O
coefficients B-climate-properties
of I-climate-properties
determination I-climate-properties
( O
R2 O
) O
of O
0.84 O
( O
p O
< O
0.001 O
) O
between O
simulated O
and O
measured O
NEP B-climate-properties
, O
providing O
a O
significant O
opportunity O
to O
reconstruct O
long O
- O
term O
climate O
change O
on O
forest B-climate-nature
C O
dynamics O
using O
only O
available O
monthly O
historical O
climate O
records O
. O

-DOCSTART- -X- O O265726580bd9e9fb2a3c3846837bba24

ENSO B-climate-nature
, O
and O
North B-climate-nature
Atlantic I-climate-nature
Oscillation I-climate-nature
– O
NAO B-climate-nature
) O
can O
be O
used O
as O
independent O
proxies O
. O

-DOCSTART- -X- O Of93f17636d6590052573f0cbbbbef1f0

evaluating O
Global B-climate-observations
Ozone I-climate-observations
Monitoring I-climate-observations
Experiment I-climate-observations
( O
GOME B-climate-observations
) O
2 O
aboard O
MetOp B-climate-observations
A I-climate-observations

-DOCSTART- -X- O O63d498f19279f868456086b7b371fbb4

Cau O
River O
Basin O
is O
located O
in O
the O
North O
- O
East O
of O
Vietnam O
. O

-DOCSTART- -X- O Of5c59104186c163acb0d0268d64205e2

European B-climate-observations
Organisation I-climate-observations
for I-climate-observations
the I-climate-observations
Exploitation I-climate-observations
of I-climate-observations
Meteorological I-climate-observations
Satellites I-climate-observations
( O
EUMETSAT B-climate-observations
) O
Satellite B-climate-organizations
Application I-climate-organizations
Facility I-climate-organizations
on I-climate-organizations
Atmospheric I-climate-organizations
Composition I-climate-organizations

-DOCSTART- -X- O O3fb7e37a4703f6b617087f9f0cd7c26a

Correlations O
between O
GOME-2A B-climate-observations
total O
ozone B-climate-nature
and O
the O
Southern B-climate-properties
Oscillation I-climate-properties
Index I-climate-properties
( O
SOI B-climate-properties
) O
were O
studied O
over O
the O
tropical B-climate-nature
Pacific O
Ocean O
after O
removing O
seasonal O
, O
QBO B-climate-nature
, O
and O
solar O
- O
cycle O
- O
related O
variability. O

-DOCSTART- -X- O O2237c0d5c8266e8c3847a6932fd77bfb

The O
cork B-climate-organisms
oak I-climate-organisms
forest B-climate-nature
is O
an O
ecosystem O
playing O
a O
major O
role O
in O
Moroccan O
socio O
- O
economy O
and O
biodiversity B-climate-organisms
conservation O
. O

-DOCSTART- -X- O O9d452e5e3c36eb4ef36180961752fc9a

Advanced O
system O
analysis O
tools O
are O
required O
to O
capture O
the O
multiple O
dimensions O
of O
these O
challenges O
: O
the O
global O
partial O
equilibrium O
model O
of O
agricultural B-climate-assets
and O
forest B-climate-nature
sectors O
, O
Global B-climate-models
Biosphere I-climate-models
Management I-climate-models
Model O
, O
developed O
at O
IIASA B-climate-organizations
, O
represents O
the O
state O
of O
the O
art O
in O
model O
linking O
across O
sectors O
, O
disciplines O
, O
and O
spatial O
scales O
. O

This O
model O
integrates O
information O
from O
a O
1x1 O
km O
grid O
where O
the O
land O
characteristics O
and O
climate O
are O
defined O
, O
up O
to O
30 O
regional O
aggregates O
where O
the O
international O
trade O
is O
represented O
. O

Crops B-climate-assets
, O
grass B-climate-nature
, O
livestock B-climate-assets
, O
and O
forest B-climate-nature
systems O
are O
parameterized O
through O
biophysical O
models O
which O
capture O
overall O
production O
and O
environmental O
impacts O
such O
as O
carbon O
and O
nitrogen O
balances O
, O
water O
use O
, O
or O
GHG O
emissions O
. O

The O
model O
can O
also O
be O
used O
for O
market O
foresight O
, O
integrated O
assessment O
of O
climate O
change O
impacts O
and O
adaptation O
, O
or O
for O
assessment O
of O
mitigation B-climate-mitigations
options O
by O
providing O
to O
energy O
system O
models O
, O
such O
as O
Model B-climate-models
for I-climate-models
Energy I-climate-models
Supply I-climate-models
Strategy I-climate-models
Alternatives I-climate-models
and I-climate-models
their I-climate-models
General I-climate-models
Environmental I-climate-models
Impact I-climate-models
( O
MESSAGE B-climate-models
) O
at O
IIASA B-climate-organizations
, O
economic O
information O
on O
abatement O
potential O
through O
emissions O
reduction O
, O
carbon O
sequestration O
and O
bioenergy B-climate-mitigations
production O
. O

-DOCSTART- -X- O Of8e8797222b309e4497b767fea74b912

After O
the O
classification O
, O
we O
adopt O
Analytical O
Hierarchy O
Process O
( O
AHP O
) O
to O
calculate O
the O
weight O
for O
each O
of O
the O
function O
and O
do O
the O
consistency O
test O
; O
second O
, O
we O
apply O
knowledge O
of O
economics O
to O
study O
the O
true O
economic O
costs O
; O
third O
, O
after O
getting O
the O
equation O
of O
the O
true O
economic O
costs O
, O
we O
realize O
it O
in O
the O
cost O
- O
benefit O
analysis O
. O

In O
order O
to O
make O
our O
work O
more O
accurate O
and O
complete O
, O
we O
use O
SWOT O
Analysis O
across O
the O
analytical O
process O
. O

-DOCSTART- -X- O O11a925eaf883b1ad3ae825dd9ccaedd3

Kazakhstan O
is O
located O
in O
the O
foothills O
of O
the O
Northern O
Tien O
Shan O
Mountains O
. O

The O
SALTMED B-climate-models
crop B-climate-assets
model O
was O
tested O
for O
its O
ability O
to O
simulate O
soil B-climate-properties
water I-climate-properties
content I-climate-properties
( O
SWC B-climate-properties
) O
, O
and O
final O
grain B-climate-properties
yield I-climate-properties
( O
Y O
) O
for O
rain B-climate-nature
- O
fed O
winter O
wheat B-climate-assets
and O
irrigated O
spring O
maize B-climate-assets
in O
2017 O
and O
2019 O
respectively O
. O

SALTMED B-climate-models
is O
able O
to O
simulate O
SWC B-climate-properties
with O
a O
high O
degree O
of O
accuracy O
in O
both O
field O
. O

-DOCSTART- -X- O O85dcb181f26fb2b534629eac1e80fcf1

The O
representation O
of O
orographic B-climate-nature
drag I-climate-nature
remains O
a O
major O
source O
of O
uncertainty O
for O
numerical O
weather O
prediction O
( O
NWP O
) O
and O
climate O
models O
. O

-DOCSTART- -X- O O0428cf5d70a488b08ff0639c610544a3

TRNSYS B-climate-models
is O
a O
powerful O
and O
flexible O
simulation O
software O
. O

-DOCSTART- -X- O O3ca9ddc511730038d0b4bf3d79bb83e2

impacts O
of O
global O
warming O
on O
Meiyu B-climate-nature
- I-climate-nature
Baiu I-climate-nature
extreme B-climate-hazards
rainfall I-climate-hazards
and O
the O
associated O
mid O
- O
latitude O
synoptic O
- O
scale O
weather O
systems O
over O
the O
Eastern O
China O
( O
EC O
) O
and O
the O
Baiu B-climate-nature
rainband I-climate-nature
( O
Bu O
) O
regions O
in O
East O
Asia O
have O
been O
examined O
, O
based O
on O
simulations O
from O
the O
20 O
- O
km O
Meteorological B-climate-organizations
Research I-climate-organizations
Institute I-climate-organizations
atmospheric O
general O
circulation O
model O
( O
MRI B-climate-models
- I-climate-models
AGCM3.2S I-climate-models
) O
. O

This O
model O
was O
demonstrated O
to O
give O
realistic O
Asian O
extreme B-climate-hazards
rainfall I-climate-hazards
, O
when O
compared O
with O
data O
from O
the O
Tropical B-climate-datasets
Rainfall I-climate-datasets
Measuring I-climate-datasets
Mission I-climate-datasets
( O
TRMM B-climate-datasets
) O
. O

The O
same O
algorithm O
was O
applied O
for O
both O
the O
present O
( O
1979 O
- O
2003 O
) O
and O
future O
( O
2075 O
- O
2099 O
) O
climate O
simulations O
from O
the O
AGCM O
, O
so O
as O
to O
infer O
the O
impacts O
of O
global O
warming O
on O
the O
behavior O
of O
these O
systems O
. O

This O
can O
be O
attributed O
to O
more O
strong O
intensity O
rainfall B-climate-nature
, O
which O
increases O
as O
the O
background B-climate-properties
temperature I-climate-properties
in O
these O
regions O
warms O
, O
largely O
following O
the O
Clausius O
- O
Clapeyron O
relation O
. O

-DOCSTART- -X- O O180861be1803604b941429205e6a4ae1

Forecasting O
is O
based O
on O
the O
data O
of O
climate O
modeling O
within O
the O
project O
CMIP3 B-climate-models
with O
« O
A2 B-climate-datasets
» O
IPCC B-climate-organizations
scenario O
. O

-DOCSTART- -X- O O42d281845a31e5edc07e4806a91aa603

Numerous O
atmosphere B-climate-nature
models O
anticipate O
an O
ideal O
storm B-climate-nature
in O
the O
2020 O
season O
( O
the O
India B-climate-organizations
Meteorological I-climate-organizations
Department I-climate-organizations
has O
additionally O
since O
formally O
reported O
) O
as O
the O
El B-climate-nature
- I-climate-nature
Nino I-climate-nature
climate O
wonder O
, O
that O
upsets O
precipitation B-climate-nature
in O
India O
, O
is O
n't O
apparent O
. O

-DOCSTART- -X- O O8f69842d729d71544edf153d45255ecb

The O
distribution O
of O
lightning B-climate-hazards
around O
the O
planet O
is O
directly O
linked O
to O
the O
Earth O
’s O
climate O
, O
which O
is O
driven O
by O
solar B-climate-properties
insolation I-climate-properties
. O

-DOCSTART- -X- O Ob6ba4d20ca052042b4fb7aecf1d3abac

Therefore O
, O
we O
considered O
reconstructed O
precipitation B-climate-nature
and O
temperature B-climate-properties
fields O
for O
the O
period O
between O
1500 O
and O
2000 O
together O
with O
reconstructed O
scPDSI B-climate-properties
, O
natural O
proxy O
data O
, O
and O
observed O
runoff B-climate-nature
over O
14 O
~ O
European O
catchments B-climate-nature
to O
calibrate O
and O
validate O
the O
semi O
- O
empirical O
hydrological B-climate-nature
model O
GR1A B-climate-models
and O
two O
data O
- O
driven O
models O
( O
Bayesian O
recurrent O
and O
long O
short O
- O
term O
memory O
neural O
network O
) O
. O

On O
the O
other O
hand O
, O
the O
data O
- O
driven O
models O
have O
been O
proven O
to O
correct O
this O
bias O
in O
many O
cases O
, O
unlike O
the O
semi O
- O
empirical O
hydrological B-climate-nature
model O
GR1A. B-climate-models

-DOCSTART- -X- O O4240e314e88ce93b118df90a14b736b9

The O
research O
investigates O
geographical O
and O
temporal O
variability O
of O
hail B-climate-hazards
incidence O
based O
on O
conventional O
stations O
reports O
on O
hail B-climate-hazards
days O
from O
1891 O
to O
2015 O
. O

-DOCSTART- -X- O Od5e44901017ee979226a004d5c530359

In O
the O
current O
study O
, O
FAO B-climate-organizations
’s O
crop B-climate-properties
water I-climate-properties
productivity I-climate-properties
model O
( O
AquaCrop B-climate-models
) O
was O
calibrated O
and O
validated O
with O
field O
data O
in O
Kabul O
River O
Basin O
( O
KRB O
) O
for O
wheat B-climate-assets
crop B-climate-assets
to O
simulate O
four O
different O
water B-climate-hazards
scarcity I-climate-hazards
scenarios O
( O
S O
- O
A O
: O

-DOCSTART- -X- O O88057bd36befbdf151337b3e02c2ce6b

The O
Glastir B-climate-organizations
Monitoring I-climate-organizations
and I-climate-organizations
Evaluation I-climate-organizations
Programme I-climate-organizations
( O
GMEP B-climate-organizations
) O
ran O
from O
2013 O
until O
2016 O
, O
and O
was O
probably O
the O
most O
comprehensive O
programme O
of O
ecological O
study O
ever O
undertaken O
at O
a O
national O
scale O
in O
Wales O
. O

As O
such O
, O
GMEP B-climate-organizations
included O
a O
large O
field O
survey O
component O
, O
collecting O
data O
on O
a O
range O
of O
elements O
including O
vegetation B-climate-nature
, O
land B-climate-properties
cover I-climate-properties
and O
use O
, O
soils B-climate-nature
, O
freshwaters B-climate-nature
, O
birds B-climate-organisms
and O
insect B-climate-organisms
pollinators I-climate-organisms
from O
up O
to O
300 O
1 O
km O
squares O
throughout O
Wales O
. O

The O
field O
survey O
capitalised O
upon O
the O
UKCEH B-climate-observations
Countryside I-climate-observations
Survey I-climate-observations
of O
Great O
Britain O
, O
which O
has O
provided O
an O
extensive O
set O
of O
repeated O
, O
standardised O
ecological O
measurements O
since O
1978 O
. O

The O
design O
of O
both O
GMEP B-climate-organizations
and O
the O
UKCEH B-climate-observations
Countryside I-climate-observations
Survey I-climate-observations
involved O
stratified O
- O
random O
sampling O
of O
squares O
from O
a O
1 O
km O
grid O
, O
ensuring O
proportional O
representation O
from O
land O
classes O
with O
distinct O
climate O
, O
geology O
and O
physical O
geography O
. O

One O
such O
repeat O
survey O
is O
scheduled O
for O
2021 O
under O
the O
environment B-climate-organizations
and I-climate-organizations
Rural I-climate-organizations
Affairs I-climate-organizations
Monitoring I-climate-organizations
and I-climate-organizations
Modelling I-climate-organizations
Programme I-climate-organizations
( O
ERAMMP B-climate-organizations
) O
. O

-DOCSTART- -X- O O4619128d132318b2de9f61e95b44e20d

We O
also O
characterized O
the O
major O
vegetation B-climate-nature
types O
of O
the O
Peninsula O
in O
the O
functional O
space O
defined O
by O
the O
NDVI B-climate-observations
dynamics O
and O
analyzed O
the O
climatic O
controls O
of O
NDVI B-climate-observations
dynamics O
. O

We O
selected O
as O
reference O
sites O
only O
homogeneous O
pixels O
occupied O
by O
natural O
vegetation B-climate-nature
. O

-DOCSTART- -X- O O5a521a11f4d38b142e36df62f3164028

parameters O
and O
related O
impacts O
on O
different O
sectors O
in O
Mali O
until O
2080 O
under O
different O
climate O
change O
scenarios O
( O
called O
Representative O
Concentration O
Pathways O
, O
RCPs O
) O
. O

RCP2.6 B-climate-datasets
represents O
the O
low O
emission B-climate-problem-origins
scenario O
in O
line O
with O
the O
Paris B-climate-mitigations
Agreement I-climate-mitigations
; O
RCP6.0 B-climate-datasets
represents O
a O
medium O
to O
high O
emission B-climate-problem-origins
scenario O
. O

Agro O
- O
ecological O
zones O
might O
shift O
, O
affecting O
ecosystems O
, O
biodiversity B-climate-organisms
and O
crop B-climate-assets
production O
. O

-DOCSTART- -X- O O465674ffe754b44ca8a65a699bb4f361

Requirements O
for O
various O
policy O
goals O
related O
to O
land B-climate-problem-origins
- I-climate-problem-origins
use I-climate-problem-origins
together O
with O
alternative B-climate-mitigations
cropping I-climate-mitigations
systems I-climate-mitigations
and O
a O
demand O
for O
agricultural B-climate-assets
produce I-climate-assets
are O
used O
to O
construct O
the O
linear O
programming O
model O
GOAL B-climate-models
( O
General B-climate-models
Optimal I-climate-models
Allocation I-climate-models
of I-climate-models
Land I-climate-models
- I-climate-models
use I-climate-models
) O
. O

The O
scenarios O
constructed O
with O
the O
aid O
of O
the O
GOAL B-climate-models
model O
explore O
technical O
possibilities O
to O
attain O
a O
set O
of O
well O
- O
founded O
policy O
objectives O
. O

-DOCSTART- -X- O O67918e0ce35e1a5b0848853e4ade4764

To O
evaluate O
its O
effectiveness O
, O
a O
case O
study O
in O
the O
Upper O
Yangtze O
River O
Basin O
( O
UYRB O
) O
of O
China O
was O
conducted O
. O

The O
results O
suggest O
that O
the O
SWAT B-climate-models
- O
based O
method O
is O
the O
best O
approach O
to O
quantify O
the O
influences O
of O
climate O
change O
and O
human O
activities O
on O
streamflow B-climate-properties
in O
the O
UYRB O
. O

-DOCSTART- -X- O O808fe0ca49048b551f82dc37df162019

In O
this O
presentation O
we O
describe O
a O
participatory O
approach O
– O
Tradeoff O
Analysis O
( O
TOA O
) O
– O
to O
the O
assessment O
of O
climate O
change O
impacts O
and O
to O
the O
design O
and O
assessment O
of O
adaptation O
strategies O
that O
incorporates O
the O
spatial O
variability O
and O
dynamics O
of O
farming B-climate-assets
systems O
. O

For O
the O
implementation O
of O
the O
quantitative O
analysis O
, O
the O
Tradeoff O
Analysis O
Software O
is O
available O
which O
integrates O
spatially O
- O
referenced O
data O
with O
crop B-climate-assets
and O
livestock B-climate-assets
simulation O
models O
, O
economic O
simulation O
models O
, O
and O
environmental O
impact O
assessment O
models O
. O

-DOCSTART- -X- O Oed9ccff4bb923cdaf91bd4c3bb02c28c

This O
submitted O
work O
analyses O
the O
impact O
of O
climate O
change O
on O
the O
wetland B-climate-nature
ecosystems O
of O
Poiplie O
, O
which O
is O
situated O
in O
the O
south O
of O
Slovakia O
in O
the O
Ipeľ O
river O
basin O
. O

The O
area O
is O
an O
important O
wetland B-climate-nature
biotope B-climate-organisms
with O
rare B-climate-organisms
plant I-climate-organisms
and I-climate-organisms
animal I-climate-organisms
species I-climate-organisms
, O
which O
mainly O
live O
in O
open B-climate-nature
water I-climate-nature
areas I-climate-nature
, O
marshes B-climate-nature
, O
wet B-climate-nature
meadows I-climate-nature
and O
alluvial B-climate-nature
forests I-climate-nature
. O

To O
evaluate O
any O
climate O
change O
, O
the O
CGCM B-climate-models
3.1 I-climate-models
model O
, O
two O
emission B-climate-problem-origins
scenarios O
, O
the O
A2 B-climate-datasets
emission B-climate-problem-origins
scenario O
( O
pessimistic O
) O
and O
the O
B1 B-climate-datasets
emission B-climate-problem-origins
scenario O
( O
optimistic O
) O
, O
were O
used O
within O
the O
regionalization O
. O

-DOCSTART- -X- O O568b06be70c55b521a42d699d615341a

In O
this O
paper O
, O
we O
present O
results O
of O
the O
2 O
reprocessing O
of O
all O
data O
from O
1996 O
to O
2014 O
from O
all O
stations O
in O
the O
European B-climate-observations
GNSS I-climate-observations
permanent I-climate-observations
network I-climate-observations
as O
performed O
at O
the O
Geodetic B-climate-organizations
Observatory I-climate-organizations
Pecný I-climate-organizations
( O
GOP B-climate-organizations
) O
. O

We O
then O
assessed O
all O
solutions O
in O
terms O
of O
the O
repeatability O
of O
coordinates O
as O
an O
internal O
evaluation O
of O
applied O
models O
and O
strategies O
, O
and O
in O
terms O
of O
zenith B-climate-properties
tropospheric I-climate-properties
delays I-climate-properties
( O
ZTD B-climate-properties
) O
and O
horizontal O
gradients O
with O
those O
of O
ERA B-climate-datasets
- I-climate-datasets
Interim I-climate-datasets
numerical O
weather O
model O
( O
NWM O
) O
reanalysis O
. O

When O
compared O
to O
the O
GOP B-climate-models
Repro1 I-climate-models
solution O
, O
the O
results O
of O
the O
GOP B-climate-models
Repro2 I-climate-models
yielded O
improvements O
of O
approximately O
50 O
% O
and O
25 O
% O
in O
the O
repeatability O
of O
the O
horizontal O
and O
vertical O
components O
, O
respectively O
, O
and O
of O
approximately O
9 O
% O
in O
tropospheric B-climate-nature
parameters O
. O

Vertical O
repeatability O
was O
reduced O
from O
4.14 O
mm O
to O
3.73 O
mm O
when O
using O
the O
VMF1 B-climate-models
mapping I-climate-models
function I-climate-models
, O
a O
priori O
ZHD B-climate-properties
, O
and O
non O
- O
tidal O
atmospheric B-climate-nature
loading O
corrections O
from O
actual O
weather O
data O
. O

-DOCSTART- -X- O Oe5e9081bae4dcd9b1282942fed124161

A O
new O
global O
model O
using O
the O
GFDL B-climate-organizations
( O
Geophysical B-climate-organizations
Fluid I-climate-organizations
Dynamics I-climate-organizations
Laboratory I-climate-organizations
) O
nonhydrostatic O
Finite B-climate-models
- I-climate-models
Volume I-climate-models
Cubed I-climate-models
- I-climate-models
Sphere I-climate-models
dynamical I-climate-models
core I-climate-models
( O
FV3 B-climate-models
) O
coupled O
to O
physical O
parameterizations O
from O
the O
National B-climate-organizations
Center I-climate-organizations
for I-climate-organizations
Environmental I-climate-organizations
Prediction I-climate-organizations
's O
Global B-climate-models
Forecast I-climate-models
System I-climate-models
( O
NCEP B-climate-organizations
/ O
GFS B-climate-models
) O
was O
built O
at O
GFDL B-climate-organizations
, O
named O
fvGFS B-climate-models
. O

This O
modern O
dynamical O
core O
has O
been O
selected O
for O
National B-climate-organizations
Oceanic I-climate-organizations
and I-climate-organizations
Atmospheric I-climate-organizations
Administration I-climate-organizations
’s O
Next B-climate-models
Generation I-climate-models
Global I-climate-models
Prediction I-climate-models
System I-climate-models
( O
NGGPS B-climate-models
) O
due O
to O
its O
accuracy O
, O
adaptability O
, O
and O
computational O
efficiency O
, O
which O
brings O
a O
great O
opportunity O
for O
the O
unification O
of O
weather O
and O
climate O
prediction O
systems O
. O

-DOCSTART- -X- O O9f13cf3962ac35ea4f6a96345e042bb3

change O
is O
expected O
to O
cause O
vegetation B-climate-nature
change O
in O
Africa O
, O
with O
profound O
impacts O
on O
ecosystems O
and O
biodiversity B-climate-organisms
. O

We O
use O
the O
adaptive B-climate-models
Dynamic I-climate-models
Global I-climate-models
Vegetation I-climate-models
Model I-climate-models
( O
aDGVM B-climate-models
) O
to O
quantify O
uncertainties O
in O
projected O
African O
vegetation B-climate-nature
until O
2099 O
. O

High O
- O
resolution O
climate O
forcing O
for O
the O
aDGVM B-climate-models
, O
was O
generated O
by O
regional O
climate O
modelling O
. O

Projections O
under O
medium O
- O
impact O
scenarios O
( O
RCP B-climate-datasets
4.5 I-climate-datasets
) O
still O
predict O
biome B-climate-organisms
changes O
for O
around O
a O
quarter O
of O
Africa O
. O

-DOCSTART- -X- O O920730102818251e9932ef6cc4dda3c0

The O
evapotranspiration B-climate-nature
is O
the O
transfer O
of O
energy O
between O
the O
Earth B-climate-nature
's I-climate-nature
surface I-climate-nature
and O
the O
atmosphere B-climate-nature
and O
it O
is O
the O
most O
productive O
mechanism O
of O
communication O
between O
the O
hydrosphere B-climate-nature
, O
lithosphere B-climate-nature
and O
biosphere B-climate-organisms
. O

This O
study O
focuses O
on O
predicting O
potential B-climate-properties
evapotranspiration I-climate-properties
in O
Golpayegan O
basin O
as O
a O
response O
to O
climate O
change O
. O

For O
this O
purpose O
, O
six O
algorithms O
including O
Hargreaves B-climate-models
- I-climate-models
Samani I-climate-models
, O
Thornthwaite B-climate-models
, O
Romanenko B-climate-models
, O
Oudin B-climate-models
, O
Kharrufa B-climate-models
and O
Blaney B-climate-models
- I-climate-models
Criddle I-climate-models
and O
also O
, O
Penman- B-climate-models
Monteith- I-climate-models
FAO I-climate-models
as O
a O
standard O
algorithm O
, O
were O
used O
for O
estimating O
the O
potential B-climate-properties
evapotranspiration I-climate-properties
. O

The O
results O
showed O
that O
the O
Hargreaves B-climate-models
- I-climate-models
Samani I-climate-models
algorithm O
performed O
closer O
to O
the O
Penman B-climate-models
- I-climate-models
Monteith I-climate-models
- I-climate-models
FAO I-climate-models
standard O
algorithm O
compared O
to O
other O
algorithms O
. O

After O
that O
, O
the O
amount O
of O
potential B-climate-properties
evapotranspiration I-climate-properties
using O
general O
circulation O
models O
( O
GCM O
) O
was O
estimated O
under O
RCP B-climate-datasets
scenarios I-climate-datasets
2.6,4.5,8.5 I-climate-datasets
for O
near O
, O
middle O
and O
far O
periods O
of O
2021 O
- O
2040 O
, O
2041 O
- O
2060 O
and O
2061 O
- O
2080 O
by O
the O
LARS B-climate-models
- I-climate-models
WG6 I-climate-models
model O
using O
the O
HadGEM2 B-climate-models
- I-climate-models
ES I-climate-models
climatic O
model O
. O

-DOCSTART- -X- O Oce8a7a8518f499344f496163d422fcd3

FORMIT B-climate-models
- I-climate-models
M I-climate-models
is O
a O
widely O
applicable O
, O
open O
- O
access O
, O
simple O
and O
flexible O
, O
climate O
- O
sensitive O
forest B-climate-assets
management I-climate-assets
simulator O
requiring O
only O
standard O
forest B-climate-nature
inventory O
data O
as O
input O
. O

The O
model O
has O
been O
linked O
to O
the O
global O
forest B-climate-assets
sector I-climate-assets
model O
EFI B-climate-models
- I-climate-models
GTM I-climate-models
to O
secure O
consistency O
between O
timber B-climate-assets
cutting O
and O
demand O
, O
although O
prescribed O
harvest B-climate-assets
scenarios O
can O
also O
be O
used O
. O

-DOCSTART- -X- O O8ac63edf94f299301894ef930cde4abd

Conceptual O
rainfall O
- O
runoff O
models O
are O
commonly O
used O
to O
estimate O
potential O
changes O
in O
runoff B-climate-nature
due O
to O
climate O
change O
. O

The O
development O
of O
these O
models O
has O
generally O
focused O
on O
reproducing O
runoff B-climate-nature
characteristics O
, O
with O
less O
scrutiny O
on O
other O
important O
processes O
such O
as O
the O
conversion O
from O
potential B-climate-properties
evapotranspiration I-climate-properties
( O
PET B-climate-properties
) O
to O
actual B-climate-properties
evapotranspiration I-climate-properties
( O
AET B-climate-properties
) O
. O

This O
study O
uses O
three O
conceptual O
rainfall O
- O
runoff O
models O
( O
GR4J B-climate-models
, O
AWBM B-climate-models
, O
and O
IHACRES_CMD B-climate-models
) O
and O
five O
catchments B-climate-nature
in O
climatologically O
different O
regions O
of O
Australia O
to O
explore O
the O
role O
of O
ET O
process O
representation O
on O
the O
sensitivity O
of O
runoff B-climate-nature
to O
plausible O
future O
changes O
in O
PET B-climate-properties
. O

The O
changes O
in O
PET B-climate-properties
were O
simulated O
using O
the O
Penman B-climate-models
- I-climate-models
Monteith I-climate-models
model I-climate-models
and O
by O
perturbing O
each O
of O
the O
driving O
variables O
( O
temperature B-climate-properties
, O
solar O
radiation O
, O
humidity B-climate-properties
, O
and O
wind B-climate-nature
) O
separately O
. O

By O
comparing O
the O
temporal O
patterns O
in O
simulated O
AET B-climate-properties
with O
eddy B-climate-nature
- O
covariance O
- O
based O
observations O
at O
two O
of O
the O
study O
locations O
, O
we O
highlighted O
some O
unrealistic O
behavior O
in O
the O
simulated O
AET B-climate-properties
from O
AWBM B-climate-models
. O

-DOCSTART- -X- O O19e0fbf2a1adb561c69306f99cc3cca9

The O
Community B-climate-models
Atmosphere I-climate-models
Model I-climate-models
( O
CAM B-climate-models
) O
is O
the O
atmospheric O
component O
of O
the O
Community B-climate-models
Climate I-climate-models
System I-climate-models
Model I-climate-models
( O
CCSM B-climate-models
) O
and O
is O
the O
primary O
consumer O
of O
computer O
resources O
in O
typical O
CCSM B-climate-models
simulations O
. O

Performance O
engineering O
has O
been O
an O
important O
aspect O
of O
CAM B-climate-models
development O
throughout O
its O
existence O
. O

-DOCSTART- -X- O O50a58565a7d03a8a759de2277af46a81

Here O
, O
we O
present O
two O
high O
- O
resolution O
( O
2 O
km O
) O
climate O
simulations O
of O
precipitation B-climate-nature
in O
the O
Alpine O
region O
, O
evaluate O
their O
performance O
over O
Switzerland O
and O
develop O
a O
new O
biascorrection O
technique O
for O
precipitation B-climate-nature
suitable O
for O
complex O
topography B-climate-nature
. O

The O
daily O
evolution O
and O
the O
annual O
cycleof O
precipitation B-climate-nature
in O
WRFERA B-climate-models
closely O
reproduces O
the O
observations O
. O

Conversely O
, O
WRFCESM B-climate-models
shows O
a O
different O
seasonality O
with O
peak O
precipitation B-climate-nature
in O
winter O
and O
not O
in O
summer O
as O
in O
the O
observations O
or O
in O
WRF B-climate-models
- I-climate-models
ERA I-climate-models
. O

-DOCSTART- -X- O O186a36dc5c5e7ce30bb8ae0865f15fcb

Over O
recent O
decades O
it O
has O
become O
clear O
that O
the O
middle B-climate-nature
atmosphere I-climate-nature
has O
a O
significant O
impact O
on O
surface B-climate-nature
and O
tropospheric B-climate-nature
climate O
. O

A O
better O
understanding O
of O
the O
middle B-climate-nature
atmosphere I-climate-nature
and O
how O
it O
reacts O
to O
the O
current O
increase O
in O
the O
concentration B-climate-properties
of O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
( O
CO2 B-climate-greenhouse-gases
) O
is O
therefore O
necessary O
. O

In O
this O
study O
, O
we O
investigate O
the O
response O
of O
the O
middle B-climate-nature
atmosphere I-climate-nature
to O
a O
doubling O
of O
the O
CO2 B-climate-properties
concentration I-climate-properties
, O
and O
the O
associated O
changes O
in O
sea B-climate-properties
surface I-climate-properties
temperatures I-climate-properties
( O
SSTs B-climate-properties
) O
, O
using O
the O
Whole B-climate-models
Atmosphere I-climate-models
Community I-climate-models
Climate I-climate-models
Model I-climate-models
( O
WACCM B-climate-models
) O
. O

We O
use O
the O
climate B-climate-models
feedback I-climate-models
response I-climate-models
analysis I-climate-models
method I-climate-models
( O
CFRAM B-climate-models
) O
to O
calculate O
the O
partial O
temperature B-climate-properties
changes I-climate-properties
due O
to O
an O
external O
forcing O
and O
climate O
feedbacks O
in O
the O
atmosphere B-climate-nature
. O

In O
this O
study O
, O
we O
discuss O
the O
direct O
forcing O
of O
CO2 B-climate-greenhouse-gases
and O
the O
effects O
of O
the O
ozone B-climate-nature
, O
water B-climate-nature
vapour I-climate-nature
, O
cloud B-climate-nature
, O
albedo B-climate-properties
and O
dynamical O
feedbacks O
. O

-DOCSTART- -X- O O5b9be7b42fa1ab22ec47916417434e0a

The O
transformation O
of O
water B-climate-nature
masses I-climate-nature
induced O
by O
air B-climate-nature
– I-climate-nature
sea I-climate-nature
fluxes I-climate-nature
in O
the O
South O
Atlantic O
Ocean O
is O
calculated O
with O
a O
global O
ocean O
model O
, O
Ocean B-climate-models
Circulation I-climate-models
and I-climate-models
Climate I-climate-models
Advanced I-climate-models
Modeling I-climate-models
( O
OCCAM B-climate-models
) O
, O
and O
has O
been O
compared O
with O
several O
observational O
datasets O
. O

Air B-climate-nature
– I-climate-nature
sea I-climate-nature
interaction O
supplies O
buoyancy B-climate-properties
to O
the O
ocean B-climate-nature
at O
almost O
all O
density O
levels O
. O

Further O
analysis O
of O
the O
buoyancy B-climate-properties
budget O
of O
the O
mixed B-climate-nature
layer I-climate-nature
in O
the O
OCCAM B-climate-models
model O
shows O
that O
diffusion O
extracts O
buoyancy B-climate-properties
from O
the O
water O
column O
at O
all O
densities O
. O

-DOCSTART- -X- O O6a134e00c03745a9240d5cb3646fdae2

Urban B-climate-mitigations
water I-climate-mitigations
management I-climate-mitigations
has O
become O
more O
challenging O
and O
expensive O
in O
the O
global O
change O
context O
. O

The O
main O
goals O
of O
this O
research O
are O
to O
( O
1 O
) O
analyse O
climate O
change O
impact O
on O
extreme B-climate-hazards
precipitation I-climate-hazards
patterns O
, O
and O
( O
2 O
) O
conduct O
iterative O
stormwater B-climate-hazards
simulation O
for O
alternative O
on O
- O
site O
stormwater B-climate-mitigations
capture I-climate-mitigations
measures O
for O
climate O
change O
adaptation O
and O
sustainable B-climate-mitigations
urban I-climate-mitigations
development I-climate-mitigations
. O

Impacts O
of O
climate O
change O
were O
investigated O
by O
considering O
precipitation B-climate-nature
projections O
of O
multiple O
GCMs O
( O
Global O
Climate O
Models O
) O
over O
Yato O
Watershed O
, O
Tokyo O
. O

Precipitation B-climate-properties
IDF I-climate-properties
curves I-climate-properties
of O
2 O
, O
5 O
, O
10 O
, O
25 O
, O
50 O
and O
100 O
- O
year O
return O
periods O
for O
present O
and O
future O
climates O
revealed O
that O
, O
for O
all O
return O
periods O
and O
durations O
, O
the O
precipitation B-climate-nature
intensities O
are O
significantly O
greater O
for O
the O
future O
climate O
than O
the O
present O
climate O
. O

The O
HEC B-climate-models
- I-climate-models
HMS I-climate-models
tool O
enabled O
simulation O
of O
flood B-climate-hazards
hydrographs O
for O
current O
and O
future O
climate O
conditions O
. O

-DOCSTART- -X- O Ob8c7f8ba5b1d7e7f3a49375f64dc1ee1

The O
costs O
of O
coastal B-climate-nature
sector O
impacts O
from O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
( O
SLR B-climate-hazards
) O
are O
an O
important O
component O
of O
the O
total O
projected O
economic O
damages B-climate-impacts
of O
climate O
change O
, O
a O
major O
input O
to O
decision O
- O
making O
and O
design O
of O
climate B-climate-mitigations
policy I-climate-mitigations
. O

The O
Coastal B-climate-models
Impact I-climate-models
and I-climate-models
Adaptation I-climate-models
Model I-climate-models
( O
CIAM B-climate-models
) O
determines O
the O
optimal O
strategy B-climate-mitigations
for I-climate-mitigations
adaptation I-climate-mitigations
at O
the O
local O
level O
, O
evaluating O
over O
12,000 O
coastal B-climate-nature
segments O
, O
as O
described O
in O
the O
DIVA B-climate-models
database O
( O
Vafeidis O
et O
al O
, O
2006 O
) O
, O
based O
on O
their O
socioeconomic O
characteristics O
and O
the O
potential O
impacts O
of O
relative O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
and O
uncertain O
storm B-climate-hazards
surge I-climate-hazards
. O

An O
application O
of O
CIAM B-climate-models
is O
then O
presented O
to O
demonstrate O
the O
model O
's O
ability O
to O
assess O
local O
impacts O
and O
direct O
costs O
, O
choose O
the O
least O
- O
cost O
adaptation O
, O
and O
estimate O
global O
net O
damages B-climate-impacts
for O
several O
probabilistic O
SLR B-climate-properties
scenarios O
( O
Kopp O
et O
al O
, O
2014 O
) O
. O

-DOCSTART- -X- O O8df3cd38c79ca46fde27c346ad00d891

In O
the O
present O
study O
, O
climate O
change O
effects O
on O
surface B-climate-nature
water I-climate-nature
availability O
and O
crop B-climate-assets
water B-climate-properties
demand I-climate-properties
( O
CWD O
) O
were O
evaluated O
in O
the O
Birr O
watershed O
( O
a O
sub O
- O
watershed B-climate-nature
of O
Abbay O
Basin O
) O
, O
Ethiopia O
. O

The O
seasonal O
and O
annual O
streamflow B-climate-properties
trends O
in O
the O
watershed B-climate-nature
were O
assessed O
using O
the O
Mann O
– O
Kendall O
( O
MK O
) O
test O
and O
Sen O
’s O
slope O
at O
5 O
% O
significance O
level O
. O

The O
surface B-climate-nature
water I-climate-nature
availability O
was O
assessed O
using O
the O
Hydrologiska B-climate-models
Byråns I-climate-models
Vattenbalansavdelning I-climate-models
( O
HBV B-climate-models
) O
model O
. O

The O
HBV B-climate-models
model O
showed O
a O
satisfactory O
performance O
during O
calibration O
( O
R2 O
= O
0.89 O
) O
and O
validation O
( O
R2 O
= O
0.85 O
) O
. O

The O
future O
projected O
streamflow B-climate-properties
indicates O
that O
minimum O
flow O
may O
decrease O
under O
RCP4.5 B-climate-datasets
and O
RCP8.5 B-climate-datasets
scenarios O
, O
revealing O
significant O
downward O
shifts O
in O
the O
years O
2035 O
and O
2055 O
, O
respectively O
. O

Current O
and O
future O
water B-climate-properties
demand I-climate-properties
for O
the O
maize B-climate-assets
crop B-climate-assets
was O
estimated O
using O
the O
CROPWAT B-climate-models
. O

-DOCSTART- -X- O O7cc41789527d4fc00487c54ee2d2d620

Flood B-climate-hazards
management O
has O
taken O
a O
new O
look O
, O
whereby O
flood B-climate-hazards
risk O
in O
rivers B-climate-nature
is O
now O
viewed O
as O
driven O
by O
not O
just O
climate O
change O
but O
also O
by O
river B-climate-nature
channel B-climate-mitigations
morphological I-climate-mitigations
adjustment I-climate-mitigations
which O
have O
been O
overlooked O
in O
the O
past O
. O

This O
study O
aimed O
at O
evaluating O
the O
contributions O
of O
channel B-climate-mitigations
morphological I-climate-mitigations
adjustment I-climate-mitigations
to O
flood B-climate-hazards
risk O
in O
rivers B-climate-nature
using O
river O
Elbe O
in O
Germany O
as O
a O
case O
study O
. O

To O
achieve O
this O
, O
an O
inundation B-climate-hazards
model O
for O
the O
June O
2013 O
flood B-climate-hazards
event O
was O
developed O
using O
the O
LISFLOOD B-climate-models
- I-climate-models
FP I-climate-models
model O
. O

It O
was O
concluded O
that O
any O
discharge B-climate-properties
rate I-climate-properties
having O
a O
return O
period O
of O
5 O
years O
( O
2544 O
m3 O
/ O
s O
) O
and O
more O
would O
likely O
exceed O
the O
water B-climate-properties
carrying I-climate-properties
capacity I-climate-properties
of O
the O
Elbe O
river O
. O

-DOCSTART- -X- O O0496fb574cc5d9dd70a923ba757a9360

Abstract O
On O
the O
basis O
of O
the O
output O
of O
3 O
General O
Circulation O
Models O
( O
GISS B-climate-models
, O
GFDL B-climate-models
, O
and O
UKMO B-climate-models
GCMs I-climate-models
) O
, O
combined O
with O
the O
local O
current O
daily O
weather O
data O
from O
1961 O
to O
2000 O
( O
Baseline O
) O
at O
19 O
sites O
and O
the O
3 O
hypotheses O
about O
increase O
in O
climatic O
variability O
( O
CV O
) O
in O
the O
future O
, O
9 O
scenarios O
of O
( O
CC+ΔCV O
) O
involving O
both O
climate O
change O
( O
CC O
) O
and O
its O
variability O
( O
ΔCV O
) O
were O
generated O
at O
the O
19 O
sites O
in O
3 O
agro O
- O
ecological O
zones O
in O
Northeast O
China O
using O
the O
Weather O
Generator O
WGEN B-climate-models
as O
a O
tool O
. O

Four O
crop B-climate-assets
models O
, O
SOYGRO B-climate-models
, O
CERES B-climate-models
- I-climate-models
Maize I-climate-models
, O
CERES B-climate-models
- I-climate-models
Wheat I-climate-models
, O
and O
CERES B-climate-models
- I-climate-models
Rice I-climate-models
in O
DSSAT B-climate-models
, O
were O
used O
as O
effect O
models O
and O
their O
parameter O
modification O
, O
validation O
, O
and O
sensitivity O
analyses O
were O
carried O
out O
using O
the O
baseline O
weather O
, O
statistical O
yield B-climate-assets
data O
of O
the O
4 O
crops B-climate-assets
, O
and O
the O
local O
typical O
soil B-climate-nature
data O
. O

-DOCSTART- -X- O O8a4a400339559292a4cea2ded7aefd5b

Summary O
: O
This O
dataset O
contains O
projections O
of O
coastal B-climate-nature
cliff B-climate-hazards
- I-climate-hazards
retreat I-climate-hazards
rates O
and O
positions O
for O
future O
scenarios O
of O
sea B-climate-hazards
- I-climate-hazards
level I-climate-hazards
rise I-climate-hazards
( O
SLR B-climate-hazards
) O
. O

Projections O
were O
made O
at O
CoSMoS B-climate-models
cross O
- O
shore B-climate-nature
transects(CST O
) O
spaced O
100 O
m O
alongshore O
. O

-DOCSTART- -X- O O1164d11e04d3d1b809a75538fd169b25

To O
better O
understand O
the O
nature O
of O
forcing O
from O
below O
on O
the O
coupled O
thermosphere B-climate-nature
/ O
ionosphere B-climate-nature
system O
, O
this O
study O
uses O
the O
Specified B-climate-models
Dynamics I-climate-models
Whole I-climate-models
Atmosphere I-climate-models
Community I-climate-models
Climate I-climate-models
Model I-climate-models
eXtended I-climate-models
( O
SD B-climate-models
- I-climate-models
WACCM I-climate-models
- I-climate-models
X I-climate-models
) O
to O
quantify O
how O
the O
meteorology O
of O
the O
underlying O
atmosphere B-climate-nature
impacts O
the O
thermosphere B-climate-nature
. O

For O
this O
study O
, O
global O
meteorological O
specifications O
are O
produced O
by O
a O
high O
- O
altitude O
version O
of O
the O
Navy B-climate-models
Global I-climate-models
Environmental I-climate-models
Model I-climate-models
( O
NAVGEM B-climate-models
- I-climate-models
HA I-climate-models
) O
, O
which O
assimilates O
standard O
meteorological O
observations O
from O
the O
surface O
through O
the O
lower B-climate-nature
atmosphere I-climate-nature
, O
and O
satellite O
- O
based O
observations O
of O
temperature B-climate-properties
and O
constituents O
in O
middle B-climate-nature
atmosphere I-climate-nature
( O
MA B-climate-nature
) O
region O
10–90 O
km O
altitude B-climate-properties
. O

Two O
SD B-climate-models
- I-climate-models
WACCM I-climate-models
- I-climate-models
X I-climate-models
simulations O
for O
the O
January O
– O
February O
2013 O
period O
are O
performed O
using O
NAVGEM B-climate-models
- I-climate-models
HA I-climate-models
specifications O
produced O
with O
and O
without O
assimilation O
. O

-DOCSTART- -X- O O931e251cfdc1e714ce8a85531e79e60a

The O
effect O
of O
fertigation B-climate-assets
regimes O
on O
wheat B-climate-assets
grown O
in O
sandy B-climate-nature
soil I-climate-nature
was O
tested O
in O
two O
field O
experiments O
in O
Egypt O
. O

The O
aim O
of O
the O
study O
was O
to O
determine O
the O
vulnerability O
of O
wheat B-climate-assets
to O
extreme B-climate-hazards
weather I-climate-hazards
event O
under O
climate O
change O
scenarios O
. O

Two O
climate O
change O
sce- O
narios O
obtained O
from O
Hadley B-climate-models
climate I-climate-models
change I-climate-models
model I-climate-models
were O
incorporated O
in O
CropSyst B-climate-models
model O
to O
assess O
wheat B-climate-assets
yield O
responses O
to O
fertigation O
regimes O
under O
these O
scenarios O
. O

-DOCSTART- -X- O O556c79532c08a40c6227017a70e06281

Long O
- O
term O
losses O
of O
soil B-climate-nature
organic I-climate-nature
carbon I-climate-nature
( O
SOC B-climate-nature
) O
have O
been O
observed O
in O
many O
agriculture B-climate-assets
lands O
in O
Northwest O
China O
, O
one O
of O
the O
regions O
with O
the O
longest O
cultivation O
history O
in O
the O
world O
. O

A O
process O
- O
based O
model O
, O
Denitrification B-climate-models
- I-climate-models
Decomposition I-climate-models
or O
DNDC B-climate-models
, O
was O
adopted O
in O
the O
study O
to O
quantify O
impacts O
of O
farming B-climate-assets
management O
practices O
on O
SOC B-climate-nature
dynamics O
for O
a O
selected O
region O
, O
Shaanxi O
Province O
. O

The O
selected O
domain O
, O
with O
3 O
million O
hectares O
of O
cropland B-climate-assets
across O
different O
climatic O
and O
farming B-climate-assets
management O
regimes O
, O
is O
representative O
for O
the O
major O
agricultural O
areas O
in O
Northwest O
China O
. O

The O
DNDC B-climate-models
model O
was O
tested O
against O
long O
- O
term O
SOC B-climate-nature
dynamics O
observed O
at O
five O
agricultural B-climate-assets
sites O
in O
China O
. O

The O
agreement O
between O
the O
observed O
and O
modeled O
results O
indicate O
that O
DNDC B-climate-models
was O
capable O
of O
capturing O
patterns O
and O
magnitudes O
of O
SOC B-climate-nature
changes O
across O
the O
climate O
zones O
, O
soil B-climate-nature
types O
, O
and O
management O
regimes O
in O
China O
. O

The O
Most O
Sensitive O
Factor O
( O
MSF O
) O
method O
was O
employed O
in O
the O
study O
to O
quantify O
the O
uncertainties O
produced O
from O
the O
upscaling O
process O
. O

-DOCSTART- -X- O O1f01c1ca6b39222d75e742092d7d1f29

A O
coupled O
general O
circulation O
model O
, O
MIROC3.2 B-climate-models
, O
is O
used O
to O
investigate O
the O
impacts O
of O
global O
warming O
on O
the O
El B-climate-nature
Nio I-climate-nature
- I-climate-nature
Southern I-climate-nature
Oscillation I-climate-nature
( O
ENSO B-climate-nature
) O
variability O
. O

-DOCSTART- -X- O O9e414b2440353255c755303925538e83

We O
investigate O
the O
value O
of O
less O
traditional O
methods O
such O
as O
Generalised O
Additive O
Models O
for O
Location O
, O
Scale O
and O
Shape O
( O
GAMLSS O
) O
adapted O
for O
time O
series O
data O
to O
accommodate O
possible O
non O
- O
linearities O
between O
herbarium O
records O
and O
year O
and/or O
climate O
; O
and O
suggest O
a O
model O
- O
free O
method O
of O
change O
- O
point O
detection O
. O

-DOCSTART- -X- O Od68f2fee190e6428052d679cf436b578

[ O
1 O
] O
Alongside O
climate O
change O
, O
anthropogenic O
emissions O
of O
CO2 B-climate-greenhouse-gases
will O
cause O
ocean B-climate-hazards
acidification I-climate-hazards
( O
OA B-climate-hazards
) O
, O
which O
will O
impact O
upon O
key O
biogeochemical B-climate-nature
processes O
in O
the O
ocean B-climate-nature
such O
as O
net B-climate-properties
primary I-climate-properties
production I-climate-properties
( O
NPP B-climate-properties
) O
, O
carbon B-climate-properties
export I-climate-properties
( O
CEX B-climate-properties
) O
, O
N2 B-climate-properties
fixation I-climate-properties
( O
NFIX B-climate-properties
) O
, O
denitrification B-climate-properties
( O
DENIT B-climate-properties
) O
, O
and O
ocean B-climate-nature
suboxia I-climate-nature
( O
SOX B-climate-nature
) O
. O

However O
, O
appraising O
the O
impact O
of O
OA O
on O
marine B-climate-nature
biogeochemical B-climate-nature
cycles I-climate-nature
requires O
ocean O
general O
circulation O
and O
biogeochemistry O
models O
( O
OGCBMs O
) O
that O
necessitate O
a O
number O
of O
assumptions O
regarding O
the O
response O
of O
phytoplankton B-climate-organisms
physiological O
processes O
to O
OA O
. O

Of O
particular O
importance O
are O
changes O
in O
C B-climate-nature
: I-climate-nature
N I-climate-nature
:P I-climate-nature
stoichiometry I-climate-nature
, O
which O
can O
not O
be O
accounted O
for O
in O
current O
generation O
OGCBMs O
that O
rely O
on O
fixed O
Redfield O
C B-climate-nature
: I-climate-nature
N I-climate-nature
:P I-climate-nature
ratios I-climate-nature
. O

We O
developed O
a O
new O
version O
of O
the O
PISCES B-climate-models
OGCBM O
that O
resolves O
the O
cycles O
of O
C O
, O
N O
, O
and O
P O
independently O
to O
investigate O
the O
impact O
of O
assumptions O
that O
OA O
( O
1 O
) O
enhances O
NPP B-climate-properties
, O
( O
2 O
) O
enhances O
losses O
of O
fixed O
carbon O
in O
dissolved O
organic O
forms O
, O
and O
( O
3 O
) O
modifies O
the O
uptake O
of O
nutrients O
by O
phytoplankton B-climate-organisms
. O

-DOCSTART- -X- O O51e16eea9a0ffa24d564deef1294e315

We O
applied O
a O
numerical O
hydrodynamic B-climate-nature
model O
( O
DYRESM B-climate-models
) O
to O
two O
large O
, O
deep O
New O
Zealand O
lakes B-climate-nature
that O
are O
characterised O
by O
deep O
thermoclines B-climate-nature
and O
high O
wind B-climate-nature
forcing O
, O
to O
assess O
their O
sensitivity O
to O
changes O
in O
climate O
. O

Predictions O
from O
downscaled O
global O
circulation O
models O
suggest O
an O
increase O
in O
mean B-climate-properties
air I-climate-properties
temperature I-climate-properties
, O
rainfall B-climate-nature
, O
and O
wind B-climate-nature
speeds I-climate-nature
. O

Compared O
to O
large O
, O
deep O
lakes B-climate-nature
in O
the O
Northern O
Hemisphere O
, O
the O
predicted O
warming O
rates O
in O
Lakes O
Wanaka O
and O
Wakatipu O
are O
slower O
, O
due O
partly O
to O
a O
lower O
predicted O
rate O
of O
atmospheric B-climate-nature
warming O
and O
the O
absence O
of O
winter O
ice B-climate-nature
cover I-climate-nature
in O
these O
lakes B-climate-nature
. O

-DOCSTART- -X- O Od9ffd2dae96f109cd4b978d80fd873da

Abstract O
Aerosol B-climate-nature
feedbacks O
are O
becoming O
more O
accepted O
as O
physical O
mechanisms O
that O
should O
be O
included O
in O
numerical O
weather O
prediction O
models O
in O
order O
to O
improve O
the O
accuracy O
of O
the O
weather O
forecasts O
. O

The O
default O
set O
- O
up O
in O
the O
Aire B-climate-organizations
Limitee I-climate-organizations
Adaptation I-climate-organizations
dynamique I-climate-organizations
Developpement I-climate-organizations
INternational I-climate-organizations
( O
ALADIN B-climate-organizations
) O
— O
High B-climate-models
Resolution I-climate-models
Limited I-climate-models
Area I-climate-models
Model I-climate-models
( O
HIRLAM B-climate-models
) O
numerical O
weather O
prediction O
system O
includes O
monthly O
aerosol B-climate-nature
climatologies O
to O
account O
for O
the O
average O
direct O
radiative O
effect O
of O
aerosols B-climate-nature
. O

This O
effect O
was O
studied O
using O
the O
default O
aerosol B-climate-nature
climatology O
in O
the O
system O
and O
compared O
to O
experiments O
run O
using O
the O
more O
up O
- O
to O
- O
date O
Max B-climate-models
- I-climate-models
Planck I-climate-models
- I-climate-models
Institute I-climate-models
Aerosol I-climate-models
Climatology I-climate-models
version I-climate-models
1 I-climate-models
( O
MACv1 B-climate-models
) O
, O
and O
time O
- O
varying O
aerosol B-climate-nature
data O
from O
the O
Monitoring B-climate-datasets
Atmospheric I-climate-datasets
Composition I-climate-datasets
and I-climate-datasets
Climate I-climate-datasets
( O
MACC B-climate-datasets
) O
reanalysis O
aerosol B-climate-nature
dataset O
. O

-DOCSTART- -X- O O023992945f87716748eeea27a0120189

Ocean B-climate-nature
biogeochemistry B-climate-nature
, O
land B-climate-nature
vegetation I-climate-nature
and O
ice B-climate-nature
sheets I-climate-nature
are O
included O
as O
components O
of O
the O
ESM O
. O

The O
Greenland O
Ice O
Sheet O
( O
GrIS O
) O
decays O
in O
all O
simulations O
, O
while O
the O
Antarctic O
ice O
sheet O
contributes O
negatively O
to O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
, O
due O
to O
enhanced O
storage O
of O
water O
caused O
by O
larger O
snowfall B-climate-nature
rates O
. O

Freshwater B-climate-nature
flux I-climate-nature
increases O
from O
Greenland O
are O
one O
order O
of O
magnitude O
smaller O
than O
total O
freshwater B-climate-nature
flux I-climate-nature
increases O
into O
the O
North O
Atlantic O
basin O
( O
the O
sum O
of O
the O
contribution O
from O
changes O
in O
precipitation B-climate-nature
, O
evaporation B-climate-nature
, O
run B-climate-nature
- I-climate-nature
off I-climate-nature
and O
Greenland O
meltwater B-climate-nature
) O
and O
do O
not O
play O
an O
important O
role O
in O
changes O
in O
the O
strength O
of O
the O
North B-climate-nature
Atlantic I-climate-nature
Meridional I-climate-nature
Overturning I-climate-nature
Circulation I-climate-nature
( O
NAMOC B-climate-nature
) O
. O

-DOCSTART- -X- O O498d7b377fc563bdcb4f9343ba303be4

Three O
major O
soil B-climate-hazards
erosion I-climate-hazards
models O
, O
including O
Environmental B-climate-models
Policy I-climate-models
Integrated I-climate-models
Climate I-climate-models
( O
EPIC B-climate-models
) O
( O
formerly O
the O
Erosion B-climate-models
- I-climate-models
Productivity I-climate-models
Impact I-climate-models
Calculator I-climate-models
) O
; O
Water B-climate-models
Erosion I-climate-models
Prediction I-climate-models
Project I-climate-models
( O
WEPP B-climate-models
) O
; O
and O
Wind B-climate-models
Erosion I-climate-models
Prediction I-climate-models
System I-climate-models
( O
WEPS B-climate-models
) O
, O
are O
reviewed O
and O
briefly O
described O
here O
. O

-DOCSTART- -X- O O4d6cd8fb54ec3ff49dd4aab6403d3478

A O
hydrologic B-climate-nature
- O
based O
forage B-climate-assets
production I-climate-assets
simulation O
model O
( O
PHYGROW B-climate-models
) O
and O
a O
population B-climate-models
mixture I-climate-models
simulation I-climate-models
model I-climate-models
( O
POPMIX B-climate-models
) O
were O
used O
respectively O
to O
simulate O
forage B-climate-assets
production I-climate-assets
and O
carrying B-climate-properties
capacity I-climate-properties
of O
a O
subtropical B-climate-nature
shrubland B-climate-nature
complex O
of O
over O
34 O
habitat B-climate-organisms
grazed O
by O
various O
ratios O
of O
cattle B-climate-assets
and O
goats B-climate-assets
with O
a O
population O
of O
indigenous B-climate-organisms
animals I-climate-organisms
( O
white B-climate-organisms
- I-climate-organisms
tailed I-climate-organisms
deer I-climate-organisms
) O
over O
a O
20 O
year O
simulated O
weather O
profile O
. O

The B-climate-models
Farm I-climate-models
Level I-climate-models
Income I-climate-models
and I-climate-models
Policy I-climate-models
Simulation I-climate-models
Model I-climate-models
( O
FLIPSIM B-climate-models
) O
were O
used O
to O
evaluate O
and O
quantify O
the O
impacts O
of O
alternative B-climate-mitigations
management I-climate-mitigations
strategies I-climate-mitigations
and O
climate O
change O
on O
grazingland B-climate-assets
ecosystems O
. O

-DOCSTART- -X- O O bpf Now looked for datasets (but more terms where something else)

-DOCSTART- -X- O O8e2c5ff2616e5b0d941d2cafbc6ed561

Rare B-climate-organisms
plant I-climate-organisms
habitat B-climate-organisms
are O
the O
most O
vulnerable O
components O
of O
vegetation B-climate-nature
cover O
under O
climate O
change O
. O

Climate O
variables O
from O
CHELSA B-climate-datasets
BIOCLIM I-climate-datasets
and O
topographic B-climate-nature
variables O
of O
the O
digital O
elevation O
model O
were O
used O
as O
predictors O
. O

-DOCSTART- -X- O O177fb96a74adc70ab4926d35ed94d4f0

In O
this O
study O
, O
we O
propose O
a O
statistical O
methodological O
framework O
to O
assess O
the O
quality O
of O
the O
EURO O
- O
CORDEX O
RCMs O
concerning O
their O
ability O
to O
simulate O
historic O
observed O
climate O
( O
temperature B-climate-properties
and O
precipitation B-climate-nature
) O
. O

In O
particular O
, O
the O
proposed O
methodology O
is O
applied O
to O
the O
Sicily O
and O
Calabria O
regions O
( O
Southern O
Italy O
) O
, O
where O
long O
historical O
precipitation B-climate-nature
and O
temperature B-climate-properties
series O
were O
recorded O
by O
the O
ground O
- O
based O
monitoring O
networks O
operated O
by O
the O
former O
Regional B-climate-organizations
Hydrographic I-climate-organizations
Offices I-climate-organizations
. O

The O
density O
of O
the O
measurements O
is O
considerably O
greater O
than O
observational O
gridded O
datasets O
available O
at O
the O
European O
level O
, O
such O
as O
E B-climate-datasets
- I-climate-datasets
OBS I-climate-datasets
or O
CRU B-climate-datasets
- I-climate-datasets
TS I-climate-datasets
. O

Results O
show O
that O
among O
the O
models O
based O
on O
the O
combination O
of O
the O
HadGEM2 B-climate-models
global O
circulation O
model O
( O
GCM O
) O
with O
the O
CLM B-climate-organizations
- I-climate-organizations
Community I-climate-organizations
RCMs O
are O
the O
most O
skillful O
in O
reproducing O
precipitation B-climate-nature
and O
temperature B-climate-properties
variability O
as O
well O
as O
drought B-climate-hazards
characteristics O
. O

-DOCSTART- -X- O O5594fa465bee5d1f8b6f55aad2508a0d

In O
total O
, O
8 O
RCM O
simulations O
were O
assessed O
against O
the O
CRU B-climate-datasets
observational O
database O
over O
different O
domains O
, O
among O
them O
two O
from O
the O
Coordinated O
Regional O
Climate O
Downscaling O
Experiment O
( O
CORDEX O
) O
. O

-DOCSTART- -X- O O73c8ca14812ca4dad153cd24878367d4

In O
this O
study O
, O
based O
on O
the O
yearly O
surface B-climate-properties
air I-climate-properties
temperature I-climate-properties
from O
the O
gridded O
CRU B-climate-datasets
TS I-climate-datasets
3.22 I-climate-datasets
dataset O
and O
the O
ensemble O
empirical O
mode O
decomposition O
method O
( O
EEMD O
) O
, O
we O
investigated O
the O
multiscale O
evolution O
of O
temperature B-climate-properties
variability O
in O
the O
arid B-climate-nature
region I-climate-nature
of O
Northwest O
China O
( O
ARNC O
) O
from O
1901 O
to O
2013 O
. O

-DOCSTART- -X- O O95d4b5e3040ae190df42a9230e58e364

The O
INDECIS B-climate-organizations
project I-climate-organizations
( O
Integrated O
approach O
for O
the O
development O
across O
Europe O
of O
user O
oriented O
climate O
indicators O
for O
GFCS B-climate-organizations
high O
- O
priority O
sectors O
: O
agriculture B-climate-assets
, O
disaster B-climate-mitigations
risk I-climate-mitigations
reduction I-climate-mitigations
, O
energy B-climate-assets
, O
health B-climate-assets
, O
water B-climate-assets
and O
tourism B-climate-assets
) O
needs O
to O
address O
the O
homogenization O
and O
quality O
control O
of O
daily O
series O
of O
the O
essential O
climatic O
variables O
stored O
in O
ECAD B-climate-datasets
. O

The O
series O
of O
two O
different O
regions O
( O
southern O
Sweden O
and O
Slovenia O
) O
are O
analyzed O
to O
evaluate O
the O
type O
, O
magnitude O
and O
frequency O
of O
the O
inhomogeneities O
to O
be O
introduced O
in O
homogeneous O
test O
series O
generated O
from O
the O
Regional O
Climate O
Model O
RACMOv2 B-climate-models
. O

After O
applying O
the O
available O
homogenization O
methods O
, O
their O
results O
will O
be O
compared O
by O
means O
of O
goodness O
of O
fit O
metrics O
and O
the O
best O
methodologies O
will O
be O
selected O
to O
debug O
the O
series O
stored O
in O
ECAD B-climate-datasets
, O
in O
order O
to O
calculate O
relevant O
climatic O
indices O
with O
which O
to O
evaluate O
the O
impact O
of O
climate O
change O
in O
priority O
economic O
sectors O
. O

-DOCSTART- -X- O Ob7bb92b65e421d9bca7d118017fcaba3

Abstract O
In O
this O
work O
we O
perform O
a O
statistical O
downscaling O
by O
applying O
a O
CDF O
transformation O
function O
to O
local O
- O
level O
daily O
precipitation B-climate-nature
extremes O
( O
from O
NCDC B-climate-organizations
station O
data O
) O
and O
corresponding O
NARCCAP B-climate-organizations
regional O
climate O
model O
( O
RCM O
) O
output O
to O
derive O
local O
- O
scale O
projections O
. O

The O
downscaling O
method O
is O
performed O
on O
58 O
locations O
throughout O
New O
England O
, O
and O
from O
the O
projected O
distribution O
of O
extreme B-climate-hazards
precipitation I-climate-hazards
local O
- O
level O
25 O
- O
year O
return O
levels O
are O
calculated O
. O

-DOCSTART- -X- O O39334ea10df6ab137dbcedad0e996f85

Differences O
between O
products O
are O
also O
identified O
, O
with O
ERA5 B-climate-datasets
- I-climate-datasets
Land I-climate-datasets
, O
HadGEM3 B-climate-models
, O
and O
BAM-1.2 B-climate-models
showing O
opposite O
interactions O
to O
satellites O
over O
parts O
of O
the O
Amazon O
and O
the O
Cerrado O
and O
stronger O
land B-climate-nature
– O
atmosphere B-climate-nature
coupling O
along O
the O
North O
Atlantic O
coast O
. O

Finally O
, O
HadGEM3 B-climate-models
and O
BAM-1.2 B-climate-models
are O
consistent O
with O
the O
median O
response O
of O
an O
ensemble O
of O
nine O
CMIP6 B-climate-models
models O
, O
showing O
they O
are O
broadly O
representative O
of O
the O
latest O
generation O
of O
climate O
models O
. O

-DOCSTART- -X- O O89e169411896b909fdd642cc501e64e4

Temporal O
stability O
is O
essential O
but O
can O
be O
altered O
by O
the O
combination O
of O
multiple O
satellite O
sensors O
and O
their O
degradation O
, O
or O
by O
the O
assimilation O
of O
new O
observations O
at O
a O
certain O
period O
in O
the O
case O
of O
reanalysis O
. O

By O
contrast O
, O
ERA5 B-climate-datasets
- I-climate-datasets
Land I-climate-datasets
is O
more O
stable O
due O
to O
the O
lack O
of O
data O
assimilation O
, O
but O
at O
expense O
of O
worsening O
its O
accuracy O
despite O
having O
a O
finer O
spatial O
resolution O
. O

The O
magnitude O
of O
most O
of O
these O
artificial O
trends O
/ O
discontinuities O
is O
larger O
than O
actual O
snow B-climate-nature
cover I-climate-nature
trends O
and O
Global B-climate-organizations
Climate I-climate-organizations
Observing I-climate-organizations
System I-climate-organizations
( O
GCOS B-climate-organizations
) O
stability O
requirements O
. O

-DOCSTART- -X- O O97e56b32c71e1ce2cce7da30f448107c

We O
assessed O
the O
performance O
of O
the O
resulting O
seasonal O
forecasts O
of O
discharge B-climate-properties
and O
water B-climate-properties
temperature I-climate-properties
by O
comparing O
them O
with O
hydrologic B-climate-nature
and O
lake B-climate-nature
( O
pseudo)observations O
( O
reanalysis O
) O
. O

We O
used O
the O
current O
seasonal B-climate-models
climate I-climate-models
forecast I-climate-models
system I-climate-models
( O
SEAS5 B-climate-models
) O
and O
reanalysis O
( O
ERA5 B-climate-datasets
) O
of O
the O
European B-climate-organizations
Centre I-climate-organizations
for I-climate-organizations
Medium I-climate-organizations
Range I-climate-organizations
Weather I-climate-organizations
Forecasts I-climate-organizations
( O
ECMWF B-climate-organizations
) O
. O

-DOCSTART- -X- O O1e56cc93e8e6dff9dbf152e673162e02

In O
this O
study O
, O
spatial O
scope O
was O
Korea O
for O
10 O
years O
from O
1981 O
to O
1990 O
. O

As O
a O
research O
method O
, O
current O
climate O
was O
estimated O
on O
the O
basis O
of O
the O
data O
obtained O
from O
observation O
at O
the O
GHCN B-climate-datasets
. O

Future O
climate O
was O
forecast O
using O
4 O
GCMs O
furnished O
by O
the O
IPCC B-climate-organizations
among O
SRES B-climate-datasets
A2 I-climate-datasets
Scenario I-climate-datasets
as O
well O
as O
the O
RCM O
received O
from O
the O
NIES B-climate-organizations
of O
Japan O
. O

Pearson O
correlation O
analysis O
was O
conducted O
for O
the O
purpose O
of O
comparing O
data O
obtained O
from O
observation O
with O
GCM O
and O
RCM O
. O

-DOCSTART- -X- O Oe946c76285e14a60702c277826b5f30b

In O
several O
biomes B-climate-organisms
, O
including O
croplands B-climate-assets
, O
wooded B-climate-nature
savannas I-climate-nature
, O
and O
tropical B-climate-nature
forests I-climate-nature
, O
many O
small O
fires B-climate-hazards
occur O
each O
year O
that O
are O
well O
below O
the O
detection O
limit O
of O
the O
current O
generation O
of O
global O
burned B-climate-properties
area I-climate-properties
products O
derived O
from O
moderate O
resolution O
surface O
reflectance O
imagery O
. O

Here O
we O
developed O
a O
preliminary O
method O
for O
combining O
1 O
- O
km O
thermal B-climate-properties
anomalies I-climate-properties
( O
active O
fires B-climate-hazards
) O
and O
500 O
m O
burned B-climate-properties
area I-climate-properties
observations O
from O
the O
Moderate B-climate-observations
Resolution I-climate-observations
Imaging I-climate-observations
Spectroradiometer I-climate-observations
( O
MODIS B-climate-observations
) O
to O
estimate O
the O
influence O
of O
these O
fires B-climate-hazards
. O

We O
estimated O
small O
fire B-climate-hazards
burned B-climate-properties
area I-climate-properties
by O
computing O
the O
difference B-climate-observations
normalized I-climate-observations
burn I-climate-observations
ratio I-climate-observations
( O
dNBR B-climate-observations
) O
for O
these O
two O
sets O
of O
active O
fires B-climate-hazards
and O
then O
combining O
these O
observations O
with O
other O
information O
. O

In O
a O
final O
step O
, O
we O
used O
the O
Global B-climate-datasets
Fire I-climate-datasets
Emissions I-climate-datasets
Database I-climate-datasets
version I-climate-datasets
3 I-climate-datasets
( O
GFED3 B-climate-datasets
) O
biogeochemical B-climate-nature
model O
to O
estimate O
the O
impact O
of O
these O
fires B-climate-hazards
on O
biomass B-climate-hazards
burning I-climate-hazards
emissions I-climate-hazards
. O

Globally O
, O
accounting O
for O
small O
fires B-climate-hazards
increased O
total O
burned B-climate-properties
area I-climate-properties
by O
approximately O
by O
35 O
% O
, O
from O
345 O
Mha O
/ O
yr O
to O
464 O
Mha O
/ O
yr O
. O

-DOCSTART- -X- O O7fc9d3290de9b05d896debb2ffa3b593

The O
spatial O
and O
temporal O
pattern O
of O
fire B-climate-hazards
activity O
is O
determined O
by O
complex O
feedbacks O
between O
climate O
and O
plant B-climate-organisms
functioning O
through O
and O
biomass B-climate-nature
desiccation I-climate-nature
, O
usually O
estimated O
by O
fire B-climate-properties
danger I-climate-properties
indices I-climate-properties
( O
FDI B-climate-properties
) O
in O
official O
fire B-climate-mitigations
risk I-climate-mitigations
prevention I-climate-mitigations
services O
. O

Contrasted O
vegetation B-climate-nature
types O
from O
fire O
- O
prone O
Brazilian O
biomes B-climate-organisms
may O
respond O
differently O
to O
soil B-climate-nature
water I-climate-nature
deficit O
during O
the O
fire B-climate-hazards
season O
. O

We O
computed O
12 O
standard O
FDIs- B-climate-properties
at O
0.5 O
° O
resolution O
from O
2002 O
to O
2011 O
and O
used O
the O
monthly O
BA B-climate-properties
from O
four O
BA B-climate-properties
datasets O
— O
from O
the O
MODIS B-climate-observations
sensor I-climate-observations
( O
MCD45A1 B-climate-observations
) O
, O
the O
MERIS B-climate-observations
sensor I-climate-observations
( O
MERIS B-climate-observations
FIRE_CCI I-climate-observations
) O
, O
the O
Global B-climate-datasets
Fire I-climate-datasets
Emission I-climate-datasets
Database I-climate-datasets
version I-climate-datasets
4 I-climate-datasets
( O
GFED4 B-climate-datasets
) O
and O
version O
4s O
including O
small O
fires B-climate-hazards
( O
GFED4s B-climate-datasets
) O
. O

We O
performed O
a O
Principal O
Component O
Analysis O
( O
PCA O
) O
on O
the O
coefficients O
of O
determination O
. O

-DOCSTART- -X- O Off47b4c15bed214b841f8f7b9035325c

Recent O
increases O
in O
the O
Natural B-climate-problem-origins
Gas I-climate-problem-origins
( O
NG B-climate-problem-origins
) O
production O
through O
hydraulic B-climate-problem-origins
fracturing I-climate-problem-origins
have O
called O
into O
question O
the O
climate O
benefit O
of O
switching O
from O
coal B-climate-problem-origins
- O
fired O
to O
natural B-climate-problem-origins
gas I-climate-problem-origins
- O
fired O
power B-climate-problem-origins
plants I-climate-problem-origins
. O

Higher O
than O
expected O
levels O
of O
methane B-climate-greenhouse-gases
, O
Non B-climate-greenhouse-gases
- I-climate-greenhouse-gases
Methane I-climate-greenhouse-gases
Hydrocarbons I-climate-greenhouse-gases
( O
NMHC B-climate-greenhouse-gases
) O
, O
and O
NOx B-climate-greenhouse-gases
have O
been O
observed O
in O
areas O
close O
to O
oil B-climate-problem-origins
and O
NG B-climate-problem-origins
operation O
facilities O
. O

We O
assessed O
the O
uncertainties O
around O
oil B-climate-problem-origins
and O
NG B-climate-problem-origins
emissions B-climate-problem-origins
by O
using O
measurements O
from O
the O
FRAPPE B-climate-observations
and O
DISCOVER B-climate-observations
- I-climate-observations
AQ I-climate-observations
campaigns O
over O
the O
Northern O
Front O
Range O
Metropolitan O
Area O
( O
NFRMA O
) O
in O
summer O
2014 O
. O

Comparison O
between O
airborne O
measurements O
and O
the O
sensitivity O
simulations O
indicates O
that O
the O
model O
- O
measurement O
bias O
of O
ethane O
ranged O
from O
−14.9 O
ppb O
to O
−8.2 O
ppb O
. O

-DOCSTART- -X- O O https://doi.org/10.3390/rs12213498

Harnessing O
the O
fire B-climate-hazards
data O
revolution O
, O
i.e. O
, O
the O
abundance O
of O
information O
from O
satellites O
, O
government O
records O
, O
social O
media O
, O
and O
human B-climate-assets
health I-climate-assets
sources O
, O
now O
requires O
complex O
and O
challenging O
data O
integration O
approaches O
. O

Here O
, O
we O
describe O
Fire B-climate-models
Events I-climate-models
Delineation I-climate-models
( O
FIRED B-climate-models
) O
, O
an O
event O
- O
delineation O
algorithm O
, O
that O
has O
been O
used O
to O
derive O
fire B-climate-hazards
events O
( O
N O
= O
51,871 O
) O
from O
the O
MODIS B-climate-datasets
MCD64 I-climate-datasets
burned B-climate-properties
area I-climate-properties
product O
for O
the O
coterminous O
US O
( O
CONUS O
) O
from O
January O
2001 O
to O
May O
2019 O
. O

The O
optimized O
spatial O
and O
temporal O
parameters O
to O
cluster O
burned B-climate-properties
area I-climate-properties
pixels O
into O
events O
were O
an O
11 O
- O
day O
window O
and O
a O
5 O
- O
pixel O
( O
2315 O
m O
) O
distance O
, O
when O
optimized O
against O
13,741 O
wildfire B-climate-hazards
perimeters O
in O
the O
CONUS O
from O
the O
Monitoring B-climate-datasets
Trends I-climate-datasets
in I-climate-datasets
Burn I-climate-datasets
Severity I-climate-datasets
record O
. O

The O
linear O
relationship O
between O
the O
size O
of O
individual O
FIRED B-climate-models
and O
Monitoring B-climate-datasets
Trends I-climate-datasets
in I-climate-datasets
Burn I-climate-datasets
Severity I-climate-datasets
( O
MTBS B-climate-datasets
) O
events O
for O
the O
CONUS O
was O
strong O
( O
R2 O
= O
0.92 O
for O
all O
events O
) O
. O

The O
open O
, O
flexible O
FIRED B-climate-models
algorithm O
could O
be O
utilized O
to O
derive O
events O
in O
any O
satellite O
product O
. O

We O
hope O
that O
this O
open O
science O
effort O
will O
help O
catalyze O
a O
community O
- O
driven O
, O
data O
- O
integration O
effort O
( O
termed O
OneFire B-climate-organizations
) O
to O
build O
a O
more O
complete O
picture O
of O
fire O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/dbc0dc3a2e8477b783960fdf80eb8e96bcf037e4

Four O
Decades O
of O
Intensifying O
Precipitation B-climate-properties
from O
Tropical B-climate-hazards
Cyclones I-climate-hazards
. O

In O
this O
study O
, O
the O
Precipitation B-climate-datasets
Estimates I-climate-datasets
from I-climate-datasets
Remotely I-climate-datasets
Sensed I-climate-datasets
Information I-climate-datasets
using I-climate-datasets
Artificial I-climate-datasets
Neural I-climate-datasets
Networks I-climate-datasets
( O
PERSIANN B-climate-datasets
) O
- O
Dynamic B-climate-datasets
Infrared I-climate-datasets
Rain I-climate-datasets
- I-climate-datasets
rate I-climate-datasets
model O
( O
PDIR B-climate-datasets
) O
product O
, O
using O
a O
consistently O
measured O
40 O
- O
year O
archive O
of O
satellite O
- O
measured O
cloud B-climate-nature
- I-climate-nature
top B-climate-nature
infrared O
temperature B-climate-properties
data O
with O
a O
spatiotemporal O
resolution O
of O
0.04 O
° O
and O
3 O
- O
hourly O
as O
forcing O
data O
, O
is O
used O
. O

The O
mean O
and O
upper O
tail O
precipitation B-climate-properties
rates O
in O
hurricanes B-climate-hazards
are O
shown O
to O
be O
rapidly O
increasing O
, O
with O
the O
greatest O
increases O
found O
in O
the O
most O
extreme O
precipitation B-climate-properties
rates O
of O
the O
strongest O
hurricanes B-climate-hazards
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/d6f2c95d7a570c7b012f0d21a1d06838a92dbbfd

ASSESSMENT O
OF O
FIRE B-climate-hazards
SEVERITY O
AND O
POST O
- O
FIRE B-climate-hazards
REGENERATION O
BASED O
ON O
TOPOGRAPHICAL B-climate-nature
FEATURES O
USING O
MULTITEMPORAL O
LANDSAT B-climate-observations
IMAGERY O
: O

A O
CASE O
STUDY O
in O
MERSIN O
, O
TURKEY O
. O

Satellite O
based O
remote O
sensing O
technologies O
and O
Geographical O
Information O
Systems O
( O
GIS O
) O
present O
operable O
and O
cost O
- O
effective O
solutions O
for O
mapping O
fires B-climate-hazards
and O
observing O
post O
- O
fire B-climate-hazards
regeneration O
. O

Mersin O
- O
Gulnar O
wildfire O
, O
which O
occurred O
in O
August O
2008 O
in O
Turkey O
, O
selected O
as O
study O
site O
. O

According O
to O
Turkish B-climate-organizations
General I-climate-organizations
Directorate I-climate-organizations
of I-climate-organizations
Forestry I-climate-organizations
reports O
, O
it O
caused O
two O
deaths B-climate-impacts
and O
left O
hundreds O
of O
people B-climate-assets
homeless B-climate-impacts
. O

Pre O
- O
fire B-climate-hazards
and O
post O
- O
fire B-climate-hazards
Landsat B-climate-observations
ETM+ B-climate-observations
images O
were O
obtained O
to O
assess O
the O
related O
fire B-climate-hazards
severity O
with O
using O
the O
widely O
- O
used O
differenced B-climate-properties
Normalized I-climate-properties
Burn I-climate-properties
Ratio I-climate-properties
( O
dNBR B-climate-properties
) O
algorithm O
. O

Also O
, O
the O
Normalized B-climate-observations
Vegetation I-climate-observations
Index I-climate-observations
( O
NDVI B-climate-observations
) O
and O
Soil B-climate-datasets
Adjusted I-climate-datasets
Vegetation I-climate-datasets
Index I-climate-datasets
( O
SAVI B-climate-datasets
) O
were O
used O
to O
determine O
vegetation B-climate-nature
regeneration O
dynamics O
for O
a O
period O
of O
six O
consecutive O
years O
. O

In O
addition O
, O
aspect B-climate-properties
image O
derived O
from O
Aster B-climate-datasets
Global I-climate-datasets
Digital I-climate-datasets
Elevation I-climate-datasets
Model I-climate-datasets
( O
GDEM B-climate-datasets
) O
were O
used O
to O
determine O
vegetation B-climate-nature
regeneration O
regime O
of O
the O
study O
area O
. O

Results O
showed O
that O
5388 O
ha O
of O
area B-climate-properties
burned I-climate-properties
with O
moderate O
to O
high O
severity O
damage B-climate-properties
. O

As O
expected O
, O
NDVI B-climate-observations
and O
SAVI B-climate-datasets
values O
distinctly O
declined O
post O
- O
fire O

-DOCSTART- -X- O O https://semanticscholar.org/paper/b1f46b9943e20066666fe831cb6a00f3a9589785

A O
Global O
Climatology O
of O
Extratropical B-climate-hazards
Transition I-climate-hazards
. O

The O
authors O
diagnose O
tropical B-climate-hazards
cyclones I-climate-hazards
that O
undergo O
extratropical B-climate-hazards
transition I-climate-hazards
( O
ET B-climate-hazards
) O
in O
the O
cyclone B-climate-hazards
phase O
space O
( O
CPS O
) O
on O
a O
global O
basis O
. O

Two O
reanalyses O
are O
employed O
and O
compared O
for O
this O
purpose O
, O
the O
Japanese B-climate-datasets
55 I-climate-datasets
- I-climate-datasets
year I-climate-datasets
Reanalysis I-climate-datasets
( O
JRA-55 B-climate-datasets
) O
and O
the O
ECMWF B-climate-datasets
Interim I-climate-datasets
Reanalysis I-climate-datasets
( O
ERA B-climate-datasets
- I-climate-datasets
Interim I-climate-datasets
) O
. O

The O
classification O
of O
ET O
storms B-climate-nature
based O
on O
JRA-55 B-climate-datasets
agrees O
better O
with O
the O
best O
- O
track O
data O
than O
does O
the O
ERA B-climate-datasets
- I-climate-datasets
Interim I-climate-datasets
classification O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/8fdf8560e95bf665cfdccda38624d8fadc9fa471

Evaluation O
of O
Accuracy O
and O
Streamflow B-climate-nature
Simulation O
of O
TRMM B-climate-datasets
Satellite O
Precipitation B-climate-properties
Data O
. O

In O
this O
study O
, O
the O
new O
precipitation B-climate-properties
product O
( O
3B42 B-climate-datasets
V7 I-climate-datasets
) O
of O
Tropical B-climate-datasets
Rainfall I-climate-datasets
Measuring I-climate-datasets
Mission I-climate-datasets
( O
TRMM B-climate-datasets
) O
was O
evaluated O
via O
comparison O
with O
the O
rain O
gauge O
precipitation B-climate-properties
data O
in O
Xiangjiang O
River O
Basin O
. O

The O
area O
precipitation B-climate-properties
of O
TRMM B-climate-datasets
data O
showed O
better O
accuracy O
in O
humid O
season O
than O
in O
arid O
season O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/71544554bdb44bb75a3d3ec01584e4591819a877

Improving O
multimodel O
weather O
forecast O
of O
monsoon B-climate-nature
rain B-climate-nature
over O
China O
using O
FSU B-climate-models
superensemble I-climate-models
. O

Our O
suite O
of O
models O
includes O
the O
operational O
suite O
selected O
by O
NCARs B-climate-organizations
TIGGE B-climate-datasets
archives O
for O
the O
THORPEX B-climate-organizations
Program O
. O

These O
are O
: O
ECMWF B-climate-organizations
, O
UKMO B-climate-models
, O
JMA B-climate-organizations
, O
NCEP B-climate-organizations
, O
CMA B-climate-organizations
, O
CMC B-climate-organizations
, O
BOM B-climate-organizations
, O
MF B-climate-organizations
, O
KMA B-climate-organizations
and O
the O
CPTEC B-climate-models
models O
. O

For O
training O
and O
forecasts O
validations O
we O
have O
made O
use O
of O
an O
advanced O
TRMM B-climate-datasets
satellite O
based O
rainfall B-climate-nature
product O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/5596a45e7728205e08e8e07d7980a3f84ab6066e

For O
doing O
this O
, O
long O
historical O
series O
of O
precipitation B-climate-properties
and O
evapotranspiration B-climate-properties
are O
considered O
; O
however O
, O
the O
sources O
of O
this O
observed O
information O
on O
land O
are O
usually O
limited O
spatially O
and O
temporally O
. O

Thus O
, O
we O
have O
evaluated O
the O
use O
of O
the O
reanalysis O
databases O
of O
the O
eartH2Observe B-climate-organizations
project O
( O
WFDEI B-climate-datasets
& O
MSWEP B-climate-datasets
) O
in O
the O
Magdalena O
- O
Cauca O
river O
basin O
in O
Colombia O
, O
through O
the O
calculation O
of O
three O
drought B-climate-hazards
indicators O
( O
SPI B-climate-properties
, O
SPEI B-climate-properties
& O
WCI B-climate-properties
) O
. O

Applying O
statistical O
and O
a O
Bootstrap O
uncertainty O
analysis O
, O
we O
evaluate O
the O
performance O
of O
the O
reanalysis O
, O
finding O
that O
the O
use O
of O
the O
MSWEP B-climate-datasets
precipitation B-climate-nature
product O
has O
a O
good O
potential O
for O
the O
analysis O
of O
droughts B-climate-hazards
in O
Colombia O

-DOCSTART- -X- O O https://semanticscholar.org/paper/1571c88ce22eaea926032a9d58e89e5a8e80b970

ASSESSMENT O
OF O
SATELLITE O
PRECIPITATION B-climate-properties
PRODUCTS O
IN O
THE O
PHILIPPINE O
ARCHIPELAGO B-climate-nature
. O

Precipitation B-climate-properties
is O
the O
most O
important O
weather B-climate-nature
parameter O
in O
the O
Philippines O
. O

Made O
up O
of O
more O
than O
7100 O
islands B-climate-nature
, O
the O
Philippine O
archipelago B-climate-nature
is O
an O
agricultural B-climate-assets
country O
that O
depends O
on O
rain B-climate-nature
- O
fed O
crops B-climate-assets
. O

Located O
in O
the O
western O
rim O
of O
the O
North O
West O
Pacific O
Ocean O
, O
this O
tropical B-climate-nature
island I-climate-nature
country O
is O
very O
vulnerable O
to O
tropical B-climate-hazards
cyclones I-climate-hazards
that O
lead O
to O
severe O
flooding B-climate-hazards
events O
. O

Recently O
, O
satellite O
- O
based O
precipitation B-climate-properties
estimates O
have O
improved O
significantly O
and O
can O
serve O
as O
alternatives O
to O
ground O
- O
based O
observations O
. O

These O
data O
can O
be O
used O
to O
fill O
data O
gaps O
not O
only O
for O
climatic O
studies O
, O
but O
can O
also O
be O
utilized O
for O
disaster B-climate-mitigations
risk I-climate-mitigations
reduction I-climate-mitigations
and O
management O
activities O
. O

This O
study O
characterized O
the O
statistical O
errors O
of O
daily O
precipitation B-climate-properties
from O
four O
satellite O
- O
based O
rainfall B-climate-nature
products O
from O
( O
1 O
) O
the O
Tropical B-climate-datasets
Rainfall I-climate-datasets
Measuring I-climate-datasets
Mission I-climate-datasets
( O
TRMM B-climate-datasets
) O
, O
( O
2 O
) O
the O
CPC B-climate-datasets
Morphing I-climate-datasets
technique I-climate-datasets
( O
CMORPH B-climate-datasets
) O
of O
NOAA B-climate-organizations
and O
( O
3 O
) O
the O
Global B-climate-datasets
Satellite I-climate-datasets
Mapping I-climate-datasets
of I-climate-datasets
Precipitation I-climate-datasets
( O
GSMAP B-climate-datasets
) O
and O
( O
4 O
) O
Precipitation B-climate-datasets
Estimation I-climate-datasets
from I-climate-datasets
Remotely I-climate-datasets
Sensed I-climate-datasets
information I-climate-datasets
using I-climate-datasets
Artificial I-climate-datasets
Neural I-climate-datasets
Networks I-climate-datasets
( O
PERSIANN B-climate-datasets
) O
. O

Results O
show O
GSMAP B-climate-datasets
to O
have O
over O
all O
lower O
bias O
and O
CMORPH B-climate-datasets
with O
lowest O
Mean O
Absolute O
Error O
( O
MAE O
) O
and O
Root O
Mean O
Square O
Error O
( O
RMSE O
) O
. O

In O
addition O
, O
a O
dichotomous O
rainfall B-climate-nature
test O
reveals O
GSMAP B-climate-datasets
and O
CMORPH B-climate-datasets
have O
low O
Proportion O
Correct O
( O
PC O
) O
for O
convective B-climate-nature
and O
stratiform B-climate-nature
rainclouds B-climate-nature
, O
respectively O
. O

TRMM B-climate-datasets
consistently O
showed O
high O
PC O
for O
almost O
all O
raincloud B-climate-nature
types O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/08b5cb16730b30ef0398e3f5302653db6d3afe62

COMPARING O
CITIES O
OF O
THE O
WORLD O
ACCORDING O
TO O
THEIR O
FOOD B-climate-assets
SECURITY I-climate-assets
RISKS O
AND O
OPPORTUNITIES O
. O

Due O
to O
the O
combined O
effect O
of O
climate O
change O
, O
expected O
population B-climate-problem-origins
growth I-climate-problem-origins
and O
increased O
concentration O
of O
population B-climate-properties
in O
cities O
and O
towns O
, O
food B-climate-impacts
insecurity I-climate-impacts
in O
urban B-climate-assets
areas I-climate-assets
is O
becoming O
of O
increasing O
concern O
and O
is O
regarded O
as O
one O
of O
the O
most O
prominent O
development O
challenges O
for O
the O
21st O
century O
. O

The O
tool O
“ O
Global B-climate-models
Metropolitan I-climate-models
Detector I-climate-models
” O
has O
been O
developed O
to O
compare O
cities O
of O
the O
world O
based O
on O
different O
dimensions O
of O
food B-climate-assets
security I-climate-assets
, O
particularly O
availability O
, O
accessibility O
, O
and O
affordability O
of O
food B-climate-assets
, O
risk O
of O
floods B-climate-hazards
and O
climate O
change O
, O
and O
healthy B-climate-assets
diets I-climate-assets
. O

Worldwide O
publicly O
available O
datasets O
, O
e.g. O
from O
FAOSTAT B-climate-datasets
, O
EarthStat B-climate-datasets
and O
WorldClim B-climate-datasets
, O
are O
used O
. O

These O
are O
separately O
converted O
( O
aggregated O
/ O
disaggregated O
) O
to O
a O
homogenous O
5 O
arc O
- O
minute O
grid O
and O
combined O
in O
the O
tool O
to O
calculate O
( O
by O
weighted O
average O
) O
and O
compare O
the O
demand O
and O
local O
supply O
of O
food B-climate-assets
, O
including O
the O
required O
area O
of O
land O
to O
meet O
the O
city O
- O
specific O
consumption O
needs O
( O
measured O
in O
“ O
Food O
Metres O
” O
) O
. O

The O
resulting O
benchmark O
of O
cities O
and O
their O
indicator O
values O
can O
be O
visualised O
in O
maps O
showing O
their O
position O
with O
respect O
to O
food B-climate-assets
security I-climate-assets
in O
general O
, O
or O
investigate O
particular O
aspects O
in O
more O
detail O
, O
e.g. O
cities O
having O
low O
/ O
high O
flood B-climate-hazards
risks O
or O
cities O
that O
are O
better O
able O
to O
meet O
the O
demand O
of O
( O
fresh O
) O
vegetables B-climate-assets
and O
fruit B-climate-assets
from O
local O
producers O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/8051bf77d31b1bc74546190bf9a91822989b4c21

White O
( O
Trifolium B-climate-organisms
repens I-climate-organisms
L. I-climate-organisms
) O
and O
Arrowleaf O
( O
Trifolium B-climate-organisms
vesiculosum I-climate-organisms
Savi I-climate-organisms
) O
Clover B-climate-organisms
Emergence O
in O
Varying O
Loblolly B-climate-organisms
Pine I-climate-organisms
( O
Pinus B-climate-organisms
taeda I-climate-organisms
L. I-climate-organisms
) O
Tree B-climate-organisms
Alley O
Spacings O
. O

Agroforestry B-climate-mitigations
systems O
have O
the O
potential O
to O
provide O
year O
- O
long O
income B-climate-assets
opportunities I-climate-assets
via O
the O
integrated O
forage B-climate-assets
or O
crop B-climate-assets
, O
timber B-climate-assets
, O
and O
livestock B-climate-assets
. O

Legumes B-climate-assets
are O
an O
attractive O
alternative O
option O
during O
the O
growing O
season O
when O
more O
traditional O
forages B-climate-assets
may O
not O
be O
as O
productive O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/ca3e1e5839cae43662262e4dfa93e6cec0e87b4d

Assessing O
Relations O
Between O
Changes O
in O
Tropical B-climate-hazards
Cyclone I-climate-hazards
Intensity O
and O
Lightning B-climate-hazards
Patterns O
Using O
GIS O
Based O
Methods O
. O
1 O
. O

Most O
previous O
efforts O
have O
focused O
on O
a O
relatively O
small O
number O
of O
storms B-climate-nature
in O
near O
- O
coastal B-climate-nature
areas O
using O
data O
from O
the O
National B-climate-datasets
Lightning I-climate-datasets
Detection I-climate-datasets
Network I-climate-datasets
( O
NLDN B-climate-datasets
) O
. O

Vaisala B-climate-organizations
’s O
recently O
developed O
Long B-climate-datasets
Range I-climate-datasets
Lightning I-climate-datasets
Detection I-climate-datasets
Network I-climate-datasets
( O
LLDN B-climate-datasets
) O
now O
allows O
us O
to O
examine O
lightning B-climate-hazards
in O
storms B-climate-nature
that O
are O
well O
offshore O
. O

The O
LLDN B-climate-datasets
consists O
of O
sensors O
from O
the O
NLDN B-climate-datasets
, O
the O
Canadian B-climate-datasets
Lightning I-climate-datasets
Detection I-climate-datasets
Network I-climate-datasets
( O
CLDN B-climate-datasets
) O
and O
long O
- O
range O
sensors O
in O
the O
North O
Pacific O
and O
Caribbean O
( O
Demetriades O
and O
Holle O
2008 O
) O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/f0f13b74d4494739e0d504c12d2abaeb88acf771

MONITORING O
BAWAKARAENG O
POST O
- O
LANDSLIDE B-climate-hazards
USING O
ALOS B-climate-observations
PALSAR B-climate-observations
DINSAR B-climate-observations
AND O
GROUND O
MEASUREMENT O
. O

Indonesia O
is O
undeniably O
true O
is O
one O
of O
the O
most O
disastrous B-climate-impacts
country O
in O
the O
world O
when O
it O
comes O
to O
incidences O
of O
natural O
disaster B-climate-impacts
. O

Besides O
its O
geologic O
setting O
of O
being O
squeezed O
by O
three O
major O
tectonic B-climate-nature
plates I-climate-nature
( O
Eurasian O
, O
Pacific O
and O
Indo O
- O
Pacific O
plates O
) O
, O
its O
location O
in O
tropical B-climate-nature
region O
makes O
it O
also O
vulnerable O
in O
terms O
weather B-climate-nature
imposed O
disaster B-climate-impacts
such O
as O
flood B-climate-hazards
, O
landslides B-climate-hazards
and O
typhoon B-climate-hazards
. O

On O
the O
other O
hand O
, O
remote O
sensing O
technology O
has O
been O
developed O
intensively O
and O
extensively O
for O
the O
use O
of O
natural O
disaster B-climate-impacts
mapping O
. O

On O
March O
24 O
, O
2004 O
, O
one O
of O
the O
major O
landslides B-climate-hazards
occurred O
at O
the O
head O
of O
Jeneberang O
River O
in O
Bawakaraeng O
Mountain O
inactive O
volcano B-climate-hazards
complex O
bringing O
huge O
amount O
of O
debris B-climate-hazards
flowing I-climate-hazards
to O
the O
lower O
stream B-climate-nature
of O
the O
river B-climate-nature
which O
endangered B-climate-hazards
the O
Bili O
- O
Bili O
Dam B-climate-mitigations
in O
the O
district O
of O
Gowa O
, O
South O
Sulawesi O
. O

The O
Dam B-climate-mitigations
is O
very O
essential O
to O
the O
city O
of O
Makassar O
for O
the O
supply O
of O
drinking B-climate-assets
water I-climate-assets
and O
electrical O
power B-climate-problem-origins
plant I-climate-problem-origins
. O

This O
study O
aims O
to O
monitor O
the O
distribution O
and O
the O
surface B-climate-properties
displacement I-climate-properties
of O
the O
uncontrolled O
collapsed O
material O
of O
the O
previous O
landslide B-climate-hazards
from O
the O
potential O
of O
material O
blockage O
to O
the O
Bili O
Bili O
Dam B-climate-mitigations
and O
possibility O
of O
future O
landslide B-climate-hazards
by O
utilizing O
the O
Japanese B-climate-observations
Advanced I-climate-observations
Land I-climate-observations
Observation I-climate-observations
Satellite I-climate-observations
( O
ALOS B-climate-observations
) O
with O
Phased B-climate-observations
Array I-climate-observations
type I-climate-observations
L I-climate-observations
- I-climate-observations
band I-climate-observations
Synthetic I-climate-observations
Aperture I-climate-observations
Radar I-climate-observations
( O
PALSAR B-climate-observations
) O
images O
in O
the O
Differential B-climate-observations
Interferometric I-climate-observations
of I-climate-observations
Synthetic I-climate-observations
Aperture I-climate-observations
Radar I-climate-observations
( O
DInSAR B-climate-observations
) O
processing O
technique O
in O
three O
consecutive O
years O
of O
2007 O
, O
2008 O
and O
2009 O
. O

With O
this O
technique O
the O
surface O
deformation O
of O
the O
landslide B-climate-hazards
area O
can O
be O
measured O
and O
validated O
with O
ground O
measurement O
of O
Global B-climate-observations
Positioning I-climate-observations
System I-climate-observations
( O
GPS B-climate-observations
) O
and O
direct O
ground O
survey O
. O

Landsat B-climate-observations
images O
were O
also O
used O
to O
analyze O
the O
spatial O
extent O
of O
the O
area O
. O

In O
conclusion O
, O
this O
study O
is O
able O
to O
show O
that O
DInSAR B-climate-observations
technique O
with O
ALOS B-climate-observations
PALSAR B-climate-observations
image O
data O
can O
be O
used O
to O
monitor O
the O
post O
landslide B-climate-hazards
area O
and O
to O
support O
the O
creation O
of O
landslide B-climate-hazards
susceptibility O
map O
of O
the O
area O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/27c837c84052fe4c7f7f473d98f85536be62ca00

The O
High B-climate-observations
- I-climate-observations
Altitude I-climate-observations
MMIC I-climate-observations
Sounding I-climate-observations
Radiometer I-climate-observations
for O
the O
Global B-climate-observations
Hawk I-climate-observations
Unmanned O
Aerial O
Vehicle O
: O
Instrument O
Description O
and O
Performance O
. O

The O
Jet B-climate-organizations
Propulsion I-climate-organizations
Laboratory I-climate-organizations
's O
High B-climate-observations
- I-climate-observations
Altitude I-climate-observations
Monolithic I-climate-observations
Microwave I-climate-observations
Integrated I-climate-observations
Circuit I-climate-observations
( I-climate-observations
MMIC I-climate-observations
) I-climate-observations
Sounding I-climate-observations
Radiometer I-climate-observations
( O
HAMSR B-climate-observations
) O
is O
a O
25 O
- O
channel O
cross O
- O
track O
scanning O
microwave O
sounder O
with O
channels O
near O
the O
60- O
and O
118 O
- O
GHz O
oxygen B-climate-properties
lines I-climate-properties
and O
the O
183 O
- O
GHz O
water B-climate-properties
- I-climate-properties
vapor I-climate-properties
line I-climate-properties
. O

It O
has O
previously O
participated O
in O
three O
hurricane B-climate-hazards
field O
campaigns O
, O
namely O
, O
CAMEX-4 B-climate-observations
( O
2001 O
) O
, O
Tropical B-climate-observations
Cloud I-climate-observations
Systems I-climate-observations
and I-climate-observations
Processes I-climate-observations
( O
2005 O
) O
, O
and O
NASA B-climate-organizations
African B-climate-observations
Monsoon I-climate-observations
Multidisciplinary I-climate-observations
Analyses I-climate-observations
( O
2006 O
) O
. O

The O
HAMSR B-climate-observations
instrument O
was O
recently O
extensively O
upgraded O
for O
the O
deployment O
on O
the O
Global B-climate-observations
Hawk I-climate-observations
( O
GH B-climate-observations
) O
unmanned O
aerial O
vehicle O
platform O
. O

One O
of O
the O
major O
upgrades O
is O
the O
addition O
of O
a O
front O
- O
end O
low O
- O
noise O
amplifier O
, O
developed O
by O
JPL B-climate-organizations
, O
to O
the O
183 O
- O
GHz O
channel O
which O
reduces O
the O
noise O
in O
this O
channel O
to O
less O
than O
0.1 O
K O
at O
the O
sensor O
resolution O
( O
~2 O
km O
) O
. O



In O
2010 O
, O
HAMSR B-climate-observations
participated O
in O
the O
NASA B-climate-organizations
Genesis I-climate-observations
and I-climate-observations
Rapid I-climate-observations
Intensification I-climate-observations
Processes I-climate-observations
campaign O
on O
the O
GH O
to O
study O
tropical B-climate-hazards
cyclone I-climate-hazards
genesis O
and O
rapid O
intensification O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/8a96f025e06acc5a3573fa621c11476e9e149df5

Evaluation O
of O
JERS-1 B-climate-observations
SAR B-climate-observations
mosaics O
for O
hydrological B-climate-nature
applications O
in O
the O
Congo O
river O
basin O
. O

Two O
JERS-1 B-climate-observations
Synthetic B-climate-observations
Aperture I-climate-observations
Radar I-climate-observations
( O
SAR B-climate-observations
) O
mosaics O
covering O
central O
Africa O
were O
investigated O
with O
respect O
to O
their O
potential O
usefulness O
for O
studies O
of O
seasonal O
flooding B-climate-hazards
dynamics O
in O
the O
Congo O
river O
basin O
. O

Stage O
data O
contemporary O
with O
the O
satellite O
acquisitions O
( O
1996 O
) O
were O
derived O
from O
the O
TOPEX B-climate-observations
/ I-climate-observations
POSEIDON I-climate-observations
Radar I-climate-observations
Altimeter I-climate-observations
and O
supplemented O
by O
historical O
in O
situ O
records O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/843025293a5f8cbf2e8bbe68a57ca2b3e14ba565

Can O
Near O
- O
Real O
- O
Time O
Satellite O
Precipitation B-climate-properties
Products O
Capture O
Rainstorms B-climate-nature
and O
Guide O
Flood B-climate-hazards
Warning O
for O
the O
2016 O
Summer O
in O
South O
China O
? O
. O

Near O
- O
real O
- O
time O
( O
NRT O
) O
satellite O
precipitation B-climate-properties
products O
are O
attractive O
to O
rainstorm B-climate-nature
monitoring O
and O
flood B-climate-hazards
warning O
guidance O
owing O
to O
its O
combination O
of O
timeliness O
, O
high O
spatiotemporal O
resolution O
, O
and O
broad O
coverage O
. O

We O
evaluate O
the O
performance O
of O
four O
NRT O
satellite O
products O
, O
i.e. O
, O
Precipitation B-climate-datasets
Estimation I-climate-datasets
from I-climate-datasets
Remotely I-climate-datasets
Sensed I-climate-datasets
Information I-climate-datasets
using I-climate-datasets
Artificial I-climate-datasets
Neural I-climate-datasets
Networks I-climate-datasets
, O
3B42RT B-climate-datasets
, O
Global B-climate-datasets
Satellite I-climate-datasets
Mapping I-climate-datasets
of I-climate-datasets
Precipitation I-climate-datasets
( O
GSMaP B-climate-datasets
) O
NRT O
, O
and O
Integrated B-climate-datasets
Multi I-climate-datasets
- I-climate-datasets
satellitE I-climate-datasets
Retrievals I-climate-datasets
for I-climate-datasets
Global I-climate-datasets
Precipitation B-climate-properties
Measurement O
( O
IMERG B-climate-datasets
) O

The O
IMERG B-climate-datasets
Late O
run O
and O
GSMaP B-climate-datasets
NRT O
perform O
the O
closest O
- O
to O
- O
ground O
observations O
. O

3B42RT B-climate-datasets
detects O
the O
most O
flood B-climate-hazards
warning O
events O
due O
to O
its O
notable O
overestimation O
of O
actual O
precipitation B-climate-properties
. O