en.val.txt

-DOCSTART- -X- O O 34f3da5c09f0dae6fa79f79176a6cc8f

The O
results O
of O
the O
aerosol B-climate-nature
retrieval O
for O
pollution B-climate-hazards
events O
featuring O
mineral B-climate-nature
dust I-climate-nature
, O
volcanic B-climate-nature
aerosol I-climate-nature
, O
biomass B-climate-hazards
burning I-climate-hazards
and O
pollution B-climate-hazards
associated O
with O
boreal O
forest B-climate-hazards
fires I-climate-hazards
are O
compared O
to O
independent O
space O
borne O
and O
ground O
based O
observations O
and O
showed O
coinciding O
temporal O
and O
spatial O
dynamics O
of O
the O
aerosol B-climate-nature
distribution O
. O

Applied O
together O
, O
these O
methods O
allow O
for O
the O
first O
time O
the O
retrieval O
of O
aerosol B-climate-nature
amount O
over O
snow B-climate-nature
and O
ice B-climate-nature
on O
local O
to O
global O
scales O
. O

-DOCSTART- -X- O O f4b313679d2ed570e0dc147900b4c39f

The O
Qilian O
Mountains O
Scientific O
Expedition O
was O
launched O
under O
the O
framework O
of O
the O
Second O
Tibetan O
Plateau O
Scientific O
Expedition O
Program O
after O
tightening O
eco B-climate-mitigations
- I-climate-mitigations
environmental I-climate-mitigations
management I-climate-mitigations
in O
the O
Qilian O
Mountains O
. O

The O
expedition O
focused O
on O
three O
key O
areas O
: O
( O
1 O
) O
Ecosystem B-climate-organisms
diversity I-climate-organisms
and O
security O
, O
( O
2 O
) O
dynamic O
changes O
in O
glaciers B-climate-nature
and O
permafrost B-climate-nature
, O
and O
( O
3 O
) O
changes O
in O
human O
activities O
and O
their O
corresponding O
eco B-climate-impacts
- I-climate-impacts
livelihood I-climate-impacts
impacts I-climate-impacts
. O

More O
than O
200 O
researchers O
participated O
in O
this O
expedition O
that O
lasted O
for O
260 O
d O
and O
spanned O
47500 O
km O
. O

First O
, O
investigations O
were O
performed O
for O
changes O
in O
human O
activities O
before O
and O
after O
tightening O
eco B-climate-mitigations
- I-climate-mitigations
environmental I-climate-mitigations
management I-climate-mitigations
in O
the O
Qilian O
Mountains O
. O

The O
local O
eco B-climate-assets
- I-climate-assets
environmental I-climate-assets
benefits I-climate-assets
and O
economic B-climate-impacts
losses I-climate-impacts
after O
the O
tightening O
eco B-climate-mitigations
- I-climate-mitigations
environmental I-climate-mitigations
management I-climate-mitigations
were O
analyzed O
by O
adopting 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
model O
and O
the O
System B-climate-models
of I-climate-models
Integrated I-climate-models
Environmental I-climate-models
and I-climate-models
Economic I-climate-models
Accounting I-climate-models
method O
. O

Second O
, O
investigations O
were O
performed O
for O
several O
glaciers B-climate-nature
located O
in O
the O
Qilian O
Mountains O
to O
evaluate O
the O
characteristics O
of O
glacier B-climate-nature
distribution O
and O
variations O
in O
glacier B-climate-nature
changes O
. O

The O
glacier B-climate-nature
ice I-climate-nature
storage I-climate-nature
and O
annual O
degradation O
rate O
were O
further O
estimated O
using O
data O
derived O
from O
a O
glacier B-climate-nature
inventory O
and O
Landsat B-climate-observations
images O
. O

Further O
, O
the O
spatial O
and O
temporal O
grassland B-climate-nature
patterns O
and O
the O
associated O
productivity O
were O
analyzed O
. O

Moreover O
, O
dynamic O
changes O
in O
the O
five O
main O
lakes B-climate-nature
in O
the O
Qilian O
Mountains O
were O
assessed O
at O
different O
spatial O
and O
temporal O
scales O
, O
and O
the O
activities O
of O
the O
key O
rare B-climate-organisms
species I-climate-organisms
such O
as O
the O
snow B-climate-organisms
leopard I-climate-organisms
were O
monitored O
regularly O
using O
infrared O
cameras O
. O

-DOCSTART- -X- O O 553120ff92ab4f5e2b219c82834aeea7

Aquatic O
biogeochemical O
models O
are O
widely O
used O
as O
tools O
for O
understanding O
aquatic B-climate-nature
ecosystems I-climate-nature
and O
predicting O
their O
response O
to O
various O
stimuli O
( O
e.g. O
, O
nutrient B-climate-problem-origins
loading I-climate-problem-origins
, O
toxic B-climate-problem-origins
substances I-climate-problem-origins
, O
climate O
change O
) O
. O

-DOCSTART- -X- O O fcdda47218ada9f03ca02477a30d9ad7

Therefore O
, O
we O
calculated O
the O
start O
, O
end O
, O
and O
length B-climate-properties
of I-climate-properties
the I-climate-properties
thermal I-climate-properties
growing I-climate-properties
season I-climate-properties
( O
SOS B-climate-properties
, O
EOS B-climate-properties
, O
and O
LOS B-climate-properties
, O
respectively O
) O
, O
which O
are O
indicators O
of O
the O
theoretical O
plant B-climate-properties
growth I-climate-properties
season I-climate-properties
, O
based O
on O
the O
daily B-climate-properties
- I-climate-properties
mean I-climate-properties
temperature I-climate-properties
of O
the O
Princeton B-climate-datasets
Global I-climate-datasets
Forcing I-climate-datasets
dataset O
from O
1948 O
to O
2016 O
. O

However O
, O
not O
all O
the O
areas O
with O
higher O
precipitation B-climate-nature
tended O
to O
have O
a O
later O
SOS B-climate-properties
, O
later O
EOS B-climate-properties
, O
and O
shorter O
LOS B-climate-properties
. O

Among O
the O
seven O
ecoregions O
, O
spatial O
synchrony O
in O
the O
SOS B-climate-properties
in O
temperate B-climate-nature
broadleaf I-climate-nature
/ I-climate-nature
mixed I-climate-nature
forests I-climate-nature
and O
temperate B-climate-nature
conifer I-climate-nature
forests I-climate-nature
changed O
the O
most O
noticeably O
, O
decreasing O
in O
both O
regions O
. O

Conversely O
, O
spatial O
synchrony O
in O
the O
EOS O
in O
the O
taiga B-climate-nature
, O
temperate B-climate-nature
grasslands I-climate-nature
/ O
savannas B-climate-nature
/ O
shrublands B-climate-nature
and O
tundra B-climate-nature
changed O
the O
most O
noticeably O
, O
increasing O
in O
each O
region O
. O

-DOCSTART- -X- O O e28a7a687f2752452efffab7e1a25517

Studies O
addressing O
climate O
variability O
during O
the O
last O
millennium O
generally O
focus O
on O
variables O
with O
a O
direct O
influence O
on O
climate O
variability O
, O
like O
the O
fast O
thermal O
response O
to O
varying O
radiative B-climate-nature
forcing I-climate-nature
, O
or O
the O
large O
- O
scale O
changes O
in O
atmospheric B-climate-nature
dynamics O
( O
e.g. O
North B-climate-nature
Atlantic I-climate-nature
Oscillation I-climate-nature
) O
. O

The O
ocean B-climate-nature
responds O
to O
these O
variations O
by O
slowly O
integrating O
in O
depth O
the O
upper O
heat O
flux O
changes O
, O
thus O
producing O
a O
delayed O
influence O
on O
ocean B-climate-properties
heat I-climate-properties
content I-climate-properties
( O
OHC B-climate-properties
) O
that O
can O
later O
impact O
low O
frequency O
SST B-climate-properties
( O
sea B-climate-properties
surface I-climate-properties
temperature I-climate-properties
) O
variability O
through O
reemergence O
processes O
. O

In O
this O
study O
, O
both O
the O
externally O
and O
internally O
driven O
variations O
of O
the O
OHC B-climate-properties
during O
the O
last O
millennium O
are O
investigated O
using O
a O
set O
of O
fully O
coupled O
simulations O
with O
the O
ECHO B-climate-models
- I-climate-models
G I-climate-models
( O
coupled O
climate O
model O
ECHAMA4 B-climate-models
and O
ocean B-climate-nature
model O
HOPE B-climate-models
- I-climate-models
G I-climate-models
) O
atmosphere O
– O
ocean O
general O
circulation O
model O
( O
AOGCM O
) O
. O

When O
compared O
to O
observations O
for O
the O
last O
55 O
yr O
, O
the O
model O
tends O
to O
overestimate O
the O
global O
trends O
and O
underestimate O
the O
decadal O
OHC B-climate-properties
variability I-climate-properties
. O

For O
instance O
, O
upper B-climate-properties
temperature I-climate-properties
in O
the O
equatorial O
Pacific O
is O
controlled O
by O
ENSO B-climate-nature
( O
El B-climate-nature
Nino I-climate-nature
Southern I-climate-nature
Oscillation I-climate-nature
) O
variability O
from O
interannual O
to O
multidecadal O
timescales O
. O

Also O
, O
both O
the O
Pacific B-climate-nature
Decadal I-climate-nature
Oscillation I-climate-nature
( O
PDO B-climate-nature
) O
and O
the O
Atlantic B-climate-nature
Multidecadal I-climate-nature
Oscillation I-climate-nature
( O
AMO B-climate-nature
) O
modulate O
intermittently O
the O
interdecadal O
OHC B-climate-properties
variability I-climate-properties
in O
the O
North O
Pacific O
and O
Mid O
Atlantic O
, O
respectively O
. O

The O
NAO B-climate-nature
, O
through O
its O
influence O
on O
North O
Atlantic O
surface B-climate-nature
heat I-climate-nature
fluxes I-climate-nature
and O
convection B-climate-nature
, O
also O
plays O
an O
important O
role O
on O
the O
OHC B-climate-properties
at O
multiple O
timescales O
, O
leading O
first O
to O
a O
cooling O
in O
the O
Labrador O
and O
Irminger O
seas O
, O
and O
later O
on O
to O
a O
North O
Atlantic O
warming O
, O
associated O
with O
a O
delayed O
impact O
on O
the O
AMO B-climate-nature
. O

-DOCSTART- -X- O O e4b5f45d2b7e71dae6b312237de6f857

To O
identify O
weather O
- O
related O
risk O
factors O
and O
their O
roles O
in O
Japanese O
encephalitis B-climate-impacts
transmission O
and O
to O
provide O
policy O
implications O
for O
local O
health O
authorities O
and O
communities O
. O

-DOCSTART- -X- O O 341c98bc38a3a14d5ef6fd8455a8eddf

Climate O
change O
is O
viewed O
as O
the O
major O
threat O
to O
the O
security O
of O
water B-climate-assets
supplies I-climate-assets
in O
most O
parts O
of O
the O
world O
in O
the O
coming O
decades O
, O
and O
the O
water B-climate-assets
resources I-climate-assets
literature O
continues O
to O
be O
dominated O
by O
impact O
and O
risk O
assessments O
based O
on O
the O
latest O
climate O
projections O
from O
General O
Circulation O
Models O
( O
GCMs O
) O
. O

More O
focus O
is O
needed O
on O
economic O
analyses O
that O
can O
inform O
the O
major O
investments O
in O
water B-climate-mitigations
use I-climate-mitigations
efficiency I-climate-mitigations
measures O
which O
can O
deliver O
the O
water B-climate-mitigations
savings I-climate-mitigations
needed O
to O
avert O
widespread O
water B-climate-hazards
scarcity I-climate-hazards
. O

-DOCSTART- -X- O O 74f64d675ff3d49210a6432b8ece5cc7

To O
characterize O
the O
urban B-climate-nature
flow I-climate-nature
, O
we O
chose O
a O
semi O
- O
empirical O
onedimensional O
model O
for O
the O
determination O
of O
urban B-climate-nature
wind I-climate-nature
speed I-climate-nature
profiles O
Nicholson O
( O
1975 O
) O
coupled O
with O
the O
AROME B-climate-models
model O
. O

-DOCSTART- -X- O O 82c4c8268087d7410e79e73fbcb2b4df

As O
compared O
with O
Coupled B-climate-models
Model I-climate-models
Intercomparison I-climate-models
Project I-climate-models
Phase I-climate-models
5 I-climate-models
( O
CMIP5 B-climate-models
) O
models O
, O
the O
future O
projection O
of O
soil B-climate-properties
moisture I-climate-properties
based O
on O
the O
latest O
CMIP6 B-climate-models
shows O
opposite O
trends O
over O
parts O
of O
China O
. O

Therefore O
, O
we O
project O
seasonal O
soil B-climate-nature
drought B-climate-hazards
over O
China O
by O
using O
the O
superensemble O
that O
includes O
a O
set O
of O
CMIP5 B-climate-models
and O
CMIP6 B-climate-models
soil B-climate-properties
moisture I-climate-properties
data O
, O
high O
resolution O
land B-climate-nature
surface I-climate-nature
simulations O
driven O
by O
bias O
- O
corrected O
CMIP5 B-climate-models
climate O
forcings O
, O
as O
wells O
large O
ensemble O
( O
LE O
) O
simulation O
data O
. O

-DOCSTART- -X- O O 3026a0ebf5c9ff526e8c25fd6baa120f

This O
article O
reports O
a O
case O
study O
that O
integrated O
traditional O
and O
scientific O
knowledge O
using O
participatory B-climate-models
three I-climate-models
- I-climate-models
dimensional I-climate-models
modeling I-climate-models
( O
P3DM B-climate-models
) O
in O
BoeBoe O
village O
, O
Solomon O
Islands O
. O

-DOCSTART- -X- O O b591f6b6f88e170361bb242cad6bb8d7

The O
Australian B-climate-models
Community I-climate-models
Climate I-climate-models
and I-climate-models
Earth I-climate-models
System I-climate-models
Simulator I-climate-models
( O
ACCESS B-climate-models
) O
has O
recently O
been O
coupled O
to O
the O
Community B-climate-models
Atmosphere I-climate-models
Biosphere I-climate-models
Land I-climate-models
Exchange I-climate-models
( O
CABLE B-climate-models
) O
model O
. O

We O
examine O
how O
this O
model O
represents O
climate B-climate-hazards
extremes I-climate-hazards
derived O
by O
the 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
and O
compare O
them O
to O
observational O
data O
sets O
using O
the O
AMIP B-climate-models
framework O
. O

-DOCSTART- -X- O O 063eb3007c7994ccefb9d9d8f74a4aa7

This O
paper O
explores O
the O
drivers O
of O
adoption O
of O
climate B-climate-mitigations
- I-climate-mitigations
smart I-climate-mitigations
agricultural I-climate-mitigations
( O
CSA B-climate-mitigations
) O
technologies O
and O
practices O
, O
taking O
into O
account O
the O
complementarity O
among O
agricultural B-climate-assets
technologies O
and O
heterogeneity O
of O
the O
farm B-climate-assets
households O
, O
using O
data O
from O
Lushoto O
in O
Tanzania O
. O

-DOCSTART- -X- O O 31f10c884d4ef93dceb71225e61e29b7

This O
study O
utilizes O
the O
National B-climate-organizations
Oceanic I-climate-organizations
and I-climate-organizations
Atmospheric I-climate-organizations
Administration I-climate-organizations
Geophysical I-climate-organizations
Fluid I-climate-organizations
Dynamics I-climate-organizations
Laboratory I-climate-organizations
three O
- O
dimensional O
global O
chemical O
transport O
model O
to O
quantify O
the O
impacts O
of O
biomass O
burning O
on O
tropospheric B-climate-nature
concentrations O
of O
carbon O
monoxide O
( O
CO O
) O
, O
nitrogen O
oxides O
( O
NOx O
) O
, O
and O
ozone O
( O
O3 O
) O
. O

-DOCSTART- -X- O O f433cd510db9e349bdf6d8764030bf47

In O
this O
paper O
we O
present O
a O
non O
- O
linear O
agricultural O
supply O
model O
for O
the O
analysis O
of O
the O
economic O
impacts O
of O
changes O
in O
crop B-climate-assets
yields I-climate-assets
due O
to O
climate O
change O
. O

-DOCSTART- -X- O O 32fa8149190f057a6b248f4ed091caff

In O
the O
current O
study O
, O
the O
reliability O
of O
the O
DiCaSM B-climate-models
was O
assessed O
when O
applied O
to O
the O
Candelaro O
catchment O
; O
those O
parameters O
that O
may O
cause O
uncertainty O
in O
model O
output O
were O
investigated O
using O
a O
generalized O
likelihood O
uncertainty O
estimation O
( O
GLUE)methodology O
. O

-DOCSTART- -X- O O cf7e872d5803ef53e829bd1740edfcf7

Model O
- O
based O
methods O
are O
very O
time O
- O
consuming O
to O
set O
up O
and O
require O
a O
good O
understanding O
of O
human O
processes O
and O
time O
series O
of O
water B-climate-problem-origins
abstraction I-climate-problem-origins
, O
land B-climate-problem-origins
use I-climate-problem-origins
change I-climate-problem-origins
, O
and O
water B-climate-assets
infrastructure I-climate-assets
and O
management O
, O
which O
often O
are O
not O
available O
. O

-DOCSTART- -X- O O 82f0b0c50c35de2f938de1ef75bde117

Three O
internally O
consistent O
socioeconomic O
scenarios O
are O
used O
to O
value O
health B-climate-assets
benefits I-climate-assets
of O
greenhouse B-climate-mitigations
gas I-climate-mitigations
mitigation I-climate-mitigations
policies I-climate-mitigations
specifically O
derived O
from O
slowing O
climate O
change O
. O

-DOCSTART- -X- O O c6215701d394cc52b35afd4d1ba4cbff

A O
study O
of O
integrated O
climate O
change O
impact O
assessment O
and O
adaptation O
study O
for O
agricultural B-climate-assets
and O
timbering B-climate-assets
activities O
in O
Mackenzie O
Basin O
, O
Canada O
, O
was O
conducted O
through O
development O
/ O
application O
of O
an O
inexact O
dynamic O
optimization O
( O
IDO O
) O
model O
that O
can O
reflect O
complex O
system O
features O
and O
a O
related O
fuzzy O
relation O
analysis O
( O
FRA O
) O
method O
that O
is O
useful O
for O
comprehensive O
assessment O
of O
impact O
patterns O
. O

-DOCSTART- -X- O O 29fc371b97724c977fbf79f0f60cb25f

Compared O
to O
CMIP3 B-climate-models
, O
CMIP5 B-climate-models
scenarios O
show O
higher O
temperature B-climate-properties
and O
wider O
ranges O
of O
changes O
in O
precipitation B-climate-nature
and O
runoff B-climate-nature
. O

-DOCSTART- -X- O O a80229cef3430a1c5a868c11f2784cc7

Within O
the O
Interreg B-climate-organizations
IVB I-climate-organizations
project I-climate-organizations
AMICE I-climate-organizations
, O
involving O
17 O
European O
partners O
, O
such O
studies O
are O
being O
conducted O
at O
the O
scale O
of O
the O
international O
catchment B-climate-nature
of O
river O
Meuse O
, O
with O
a O
focus O
on O
the O
Vesdre O
reservoirs O
in O
Belgium O
and O
the O
Rur O
reservoirs O
in O
Germany O
. O

-DOCSTART- -X- O O 7c519e0cf445c8b8223075909e478b77

We O
analyzed O
projected O
climate O
change O
in O
four O
basins B-climate-nature
, O
quantified O
climate O
change O
impact O
on O
annual O
and O
seasonal O
runoff B-climate-nature
based O
on O
the O
Soil B-climate-models
Water I-climate-models
Assessment I-climate-models
Tool I-climate-models
, O
and O
estimated O
the O
uncertainty O
constrained O
by O
the O
global O
circulation O
models O
( O
GCMs O
) O
structure O
and O
the O
Representative O
Concentration O
Pathways O
( O
RCPs O
) O
. O

This O
led O
to O
projected O
precipitation B-climate-nature
increase O
by O
about O
2 O
% O
for O
the O
four O
basins B-climate-nature
, O
and O
to O
a O
decrease O
in O
simulated O
annual O
runoff I-climate-nature
of O
8 O
% O
and O
1 O
% O
in O
the O
Shiyang O
and O
Huaihe O
rivers O
, O
respectively O
, O
but O
to O
an O
increase O
of O
4 O
% O
in O
the O
Chaobai O
and O
Fujiang O
rivers O
. O

The O
uncertainty O
in O
projected O
annual B-climate-properties
temperature I-climate-properties
was O
dominated O
by O
the O
GCMs O
or O
the O
RCPs O
; O
however O
, O
that O
of O
precipitation B-climate-nature
was O
constrained O
mainly O
by O
the O
GCM O
. O

-DOCSTART- -X- O O 16a36fd1c3c447dc736546bb7caa88a7

In O
the O
second O
, O
the O
existing O
system O
’s O
response O
to O
both O
today O
’s O
and O
future O
design O
storms B-climate-nature
are O
simulated O
by O
a O
coarse B-climate-models
sewer I-climate-models
model I-climate-models
setup I-climate-models
( O
MOUSE B-climate-models
) O
and O
a O
detailed B-climate-models
coupled I-climate-models
surface I-climate-models
- I-climate-models
sewer I-climate-models
model I-climate-models
setup I-climate-models
( O
TSR B-climate-models
) O
. O

-DOCSTART- -X- O O b2c46d82be9d48a2af518a7dbc1f0227

In O
order O
to O
complete O
the O
analysis O
, O
the O
downscaled O
scenario O
from O
ENSEMBLES B-climate-models
was O
also O
used O
with O
the O
datasets O
of O
49 O
weather O
stations O
from O
FEM B-climate-organizations
and O
the O
“ O
RMAWGEN B-climate-models
” O
packages O
( O
Cordano O
et O
al O
. O
, O
2012 O
) O
created O
for O
this O
project O
in O
R O
statistical O
open O
source O
software O
( O
Gentleman O
et O
al O
. O
, O
1997 O
) O
. O

-DOCSTART- -X- O O f2ca4b0adb5d8b239b010cd0bcb7ae5f

forced O
with O
sea B-climate-properties
surface I-climate-properties
temperature I-climate-properties
and O
sea B-climate-nature
ice I-climate-nature
for O
the O
period O
2061 O
- O
2090 O
from O
the O
CMIP3 B-climate-models
HadGEM1 B-climate-models
experiments O
. O

Here O
we O
use O
an O
RCM O
at O
50 O
km O
resolution O
over O
the O
Arctic O
and O
25 O
km O
over O
Svalbard O
, O
which O
captures O
well O
the O
present O
- O
day O
pattern O
of O
precipitation B-climate-nature
and O
provides O
a O
detailed O
picture O
of O
the O
projected O
changes O
in O
the O
behaviour O
of O
the O
oceanic B-climate-nature
- I-climate-nature
atmosphere I-climate-nature
moisture I-climate-nature
fluxes I-climate-nature
and O
how O
they O
affect O
precipitation B-climate-nature
. O

-DOCSTART- -X- O O f5160c9383a19e501ba419589d9131af

Aim O
Ixodes B-climate-hazards
scapularis I-climate-hazards
is O
the O
most O
important O
vector O
of O
human O
tick B-climate-hazards
- I-climate-hazards
borne I-climate-hazards
pathogens I-climate-hazards
in O
the O
United O
States O
, O
which O
include O
the O
agents O
of O
Lyme B-climate-impacts
disease I-climate-impacts
, O
human O
babesiosis B-climate-impacts
and O
human O
anaplasmosis B-climate-impacts
, O
among O
others O
. O

The O
density B-climate-properties
of O
host O
- O
seeking O
I. B-climate-hazards
scapularis I-climate-hazards
nymphs O
is O
an O
important O
component O
of O
human O
risk O
for O
acquiring O
Borrelia B-climate-hazards
burgdorferi I-climate-hazards
, O
the O
aetiological O
agent O
of O
Lyme B-climate-impacts
disease I-climate-impacts
. O

-DOCSTART- -X- O O dc537843e218b03825aaeb78140d0107

The O
retrieval O
of O
both O
height B-climate-properties
and O
velocity B-climate-properties
of O
a O
volcanic B-climate-nature
plume I-climate-nature
is O
an O
important O
issue O
in O
volcanology B-climate-hazards
. O

As O
an O
example O
, O
it O
is O
known O
that O
large O
volcanic B-climate-hazards
eruptions I-climate-hazards
can O
temporarily O
alter O
the O
climate O
, O
causing O
global O
cooling O
and O
shifting O
precipitation B-climate-nature
patterns O
; O
the O
ash B-climate-nature
/ I-climate-nature
gas I-climate-nature
dispersion I-climate-nature
in O
the O
atmosphere B-climate-nature
, O
their O
impact O
and O
lifetime B-climate-properties
around O
the O
globe O
, O
greatly O
depends O
on O
the O
injection B-climate-properties
altitude I-climate-properties
. O

Knowing O
the O
plume B-climate-properties
altitude I-climate-properties
is O
also O
important O
to O
get O
the O
correct O
amount O
of O
SO O
2 O
concentration B-climate-properties
from O
dedicated O
spaceborne O
spectrometers O
. O

Satellite O
remote O
sensing O
offers O
a O
comprehensive O
and O
safe O
way O
to O
estimate O
plume B-climate-properties
height I-climate-properties
. O

-DOCSTART- -X- O O 3cf0b09fe9a344001226ada9481e10b7

Abstract O
Three O
extensive O
global O
wind B-climate-properties
speed I-climate-properties
and O
wave B-climate-properties
height I-climate-properties
datasets O
( O
altimeter O
, O
radiometer O
, O
model O
reanalysis O
) O
are O
analysed O
to O
investigate O
the O
global O
wind B-climate-properties
speed I-climate-properties
and O
wave B-climate-properties
height I-climate-properties
climate O
. O

At O
high O
latitudes O
both O
altimeter O
and O
radiometer O
winds B-climate-nature
are O
biased O
high O
compared O
to O
buoy O
measurements O
. O

As O
winds B-climate-nature
have O
a O
diurnal O
variation O
in O
magnitude O
, O
this O
preferential O
measurement O
time O
can O
also O
bias O
statistical O
values O
obtained O
from O
such O
satellite O
systems O
. O

-DOCSTART- -X- O O 7e58ae12fa69f0ce7a4a033807fb04a7

An O
intermediate O
complexity O
atmospheric O
general O
circulation O
model O
has O
been O
used O
to O
investigate O
the O
influence O
of O
the O
South B-climate-nature
Atlantic I-climate-nature
Ocean I-climate-nature
( I-climate-nature
SAO I-climate-nature
) I-climate-nature
dipole I-climate-nature
( O
SAOD B-climate-nature
) O
on O
summer O
precipitation B-climate-nature
over O
the O
Guinea O
Coast O
of O
West O
Africa O
. O

Consistently O
, O
above O
( O
below O
) O
the O
average O
precipitation B-climate-nature
is O
simulated O
over O
the O
Guinea O
Coast O
during O
the O
positive O
( O
negative O
) O
phase O
of O
the O
SAOD B-climate-nature
. O

During O
the O
SAOD B-climate-nature
, O
cool O
SST B-climate-properties
anomaly I-climate-properties
in O
the O
extra B-climate-nature
- I-climate-nature
tropical I-climate-nature
SAO O
off O
the O
Brazil O
– O
Uruguay O
– O
Argentina O
coast O
gives O
rise O
to O
suppressed O
convection B-climate-nature
and O
mass O
divergence O
. O

-DOCSTART- -X- O O 9438617e54eba9376866f3b132100bd7

island B-climate-nature
of O
Tenerife O
, O
a O
UNESCO B-climate-organizations
Biosphere B-climate-mitigations
Reserve I-climate-mitigations
in O
the O
Atlantic O
Ocean O
, O
aims O
to O
be O
energy O
self O
- O
sufficient O
in O
order O
to O
reduce O
its O
carbon B-climate-properties
footprint I-climate-properties
. O

To O
accomplish O
this O
goal O
it O
should O
develop O
the O
renewable O
sources O
, O
in O
particular O
wave B-climate-mitigations
and I-climate-mitigations
offshore I-climate-mitigations
wind I-climate-mitigations
energy I-climate-mitigations
. O

-DOCSTART- -X- O O 0b6944158cf45f4e2eb8b0ba80c9035f

Droughts B-climate-hazards
are O
disproportionately O
impacting O
global O
dryland B-climate-nature
regions O
where O
ecosystem O
health O
and O
function O
are O
tightly O
coupled O
to O
moisture B-climate-properties
availability O
. O

Drought B-climate-hazards
severity O
is O
commonly O
estimated O
using O
algorithms O
such O
as O
the O
standardized B-climate-properties
precipitation I-climate-properties
- I-climate-properties
evapotranspiration I-climate-properties
index I-climate-properties
( O
SPEI B-climate-properties
) O
, O
which O
can O
estimate O
climatic O
water B-climate-nature
balance I-climate-nature
impacts O
at O
various O
hydrologic O
scales O
by O
varying O
computational O
length O
. O

In O
this O
study O
, O
we O
tested O
components O
of O
climatic O
water B-climate-nature
balance I-climate-nature
, O
including O
SPEI B-climate-properties
and O
SPEI B-climate-properties
computation O
lengths O
, O
to O
recreate O
multi O
- O
decadal O
and O
periodic O
soilmoisture B-climate-nature
patterns O
across O
soil O
profiles O
at O
866 O
sites O
in O
the O
western O
United O
States O
. O

-DOCSTART- -X- O O8472d27b2066e5fea960c17702d56df9

European B-climate-organizations
Commission I-climate-organizations
have O
developed O
a O
long O
- O
term O
energy O
strategy O
that O
, O
if O
successful O
, O
will O
result O
in O
net B-climate-mitigations
- I-climate-mitigations
zero I-climate-mitigations
greenhouse I-climate-mitigations
gas I-climate-mitigations
emissions I-climate-mitigations
in O
Europe O
. O

-DOCSTART- -X- O Of6a9335da7820f8119c7fd81b0e1ce68

Estuaries B-climate-nature
are O
impacted O
by O
multiple O
anthropogenic O
stressors O
from O
eutrophication B-climate-hazards
to O
climate O
change O
. O

Long O
- O
term O
observational O
datasets O
allow O
the O
determination O
of O
trends O
in O
estuarine B-climate-nature
indicators O
and O
the O
prediction O
of O
future O
conditions O
. O

Here O
, O
a O
dataset O
of O
water B-climate-assets
quality I-climate-assets
and O
demersal B-climate-organisms
fish I-climate-organisms
community O
composition O
in O
a O
Long O
Island O
Sound O
embayment O
( O
Norwalk O
Harbor O
, O
Connecticut O
) O
from O
1987 O
to O
2016 O
was O
examined O
. O

-DOCSTART- -X- O O35a1108710496848025f53ecd78137f1

The O
aim O
of O
our O
study O
was O
to O
determine O
global O
soil B-climate-nature
organic I-climate-nature
carbon I-climate-nature
( O
SOC B-climate-nature
) O
change O
patterns O
after O
LUC O
and O
to O
assess O
the O
impacts O
of O
both O
biophysical O
and O
socioeconomic O
factors O
that O
influence O
stocks O
of O
SOC B-climate-nature
after O
LUC O
simultaneously O
. O

However O
, O
also O
, O
socioeconomic O
variables O
such O
as O
indices O
of O
poverty B-climate-impacts
, O
population B-climate-problem-origins
growth I-climate-problem-origins
, O
and O
levels O
of O
corruption B-climate-problem-origins
were O
important O
. O

-DOCSTART- -X- O O9c20de8788e8d7e01e05d3d8463edbb0

Urban B-climate-hazards
heat I-climate-hazards
island I-climate-hazards
( O
UHI B-climate-hazards
) O
effect O
, O
the O
side O
effect O
of O
rapid O
urbanization B-climate-problem-origins
, O
has O
become O
an O
obstacle O
to O
the O
further O
healthy O
development O
of O
the O
city O
. O

For O
this O
purpose O
, O
the O
geographically O
- O
weighted O
regression O
( O
GWR O
) O
approach O
is O
used O
to O
explore O
the O
scale O
effects O
in O
a O
mountainous O
city O
, O
namely O
the O
change O
laws O
and O
characteristics O
of O
the O
relationships O
between O
land B-climate-properties
surface I-climate-properties
temperature I-climate-properties
and O
impact O
factors O
at O
different O
spatial O
resolutions O
( O
30–960 O
m O
) O
. O

The O
impact O
factors O
include O
the O
Soil B-climate-datasets
- I-climate-datasets
adjusted I-climate-datasets
Vegetation I-climate-datasets
Index I-climate-datasets
( O
SAVI B-climate-datasets
) O
, O
the O
Index B-climate-datasets
- I-climate-datasets
based I-climate-datasets
Built I-climate-datasets
- I-climate-datasets
up I-climate-datasets
Index I-climate-datasets
( O
IBI B-climate-datasets
) O
, O
and O
the O
Soil B-climate-datasets
Brightness I-climate-datasets
Index I-climate-datasets
( O
NDSI B-climate-datasets
) O
, O
which O
indicate O
the O
coverage O
of O
the O
vegetation B-climate-nature
, O
built B-climate-assets
- I-climate-assets
up I-climate-assets
, O
and O
bare B-climate-nature
land I-climate-nature
, O
respectively O
. O

Results O
from O
the O
experiment O
exemplified O
by O
Chongqing O
showed O
that O
the O
GWR O
approach O
had O
a O
better O
prediction O
accuracy O
and O
a O
better O
ability O
to O
describe O
spatial O
non O
- O
stationarity O
than O
the O
OLS O
approach O
judged O
by O
the O
analysis O
of O
the O
local O
coefficient O
of O
determination O
( O
R2 O
) O
, O
Corrected O
Akaike O
Information O
Criterion O
( O
AICc O
) O
, O
and O
F O
- O
test O
at O
small O
spatial O
resolution O
( O
< O
240 O
m O
) O
; O
however O
, O
when O
the O
spatial O
scale O
was O
increased O
to O
480 O
m O
, O
this O
advantage O
has O
become O
relatively O
weak O
. O

-DOCSTART- -X- O O0db452927abef4a6d15c93618e068c7a

We O
analyse O
various O
observational O
data O
sets O
in O
order O
to O
assess O
and O
to O
compare O
th O
spatio O
- O
temporal O
characteristics O
and O
intensity B-climate-properties
of O
the O
Sahel O
flood O
in O
2007 O
and O
the O
associated O
rain B-climate-nature
events O
. O

-DOCSTART- -X- O Oc3c945c3bfd4abaf083821635cdf2025

This O
report O
gives O
a O
summary O
of O
the O
results O
of O
the O
research O
project O
“ O
Future B-climate-organizations
of I-climate-organizations
Finnish I-climate-organizations
energy I-climate-organizations
business I-climate-organizations
– I-climate-organizations
scenarios I-climate-organizations
and I-climate-organizations
strategies I-climate-organizations
” O
( O
SALKKU B-climate-organizations
) O
. O

The O
SALKKU B-climate-organizations
research O
was O
carried O
out O
as O
a O
joint O
research O
project O
of O
VTT B-climate-organizations
Technical I-climate-organizations
Research I-climate-organizations
Centre I-climate-organizations
of O
Finland O
( O
VTT B-climate-organizations
) O
and O
MTT B-climate-organizations
Agrifood I-climate-organizations
Research I-climate-organizations
Finland O
( O
MTT B-climate-organizations
) O
. O

The O
demand O
for O
energy O
was O
studied O
on O
a O
global O
, O
an O
EU B-climate-organizations
, O
and O
especially O
on O
an O
Asian O
level O
. O

-DOCSTART- -X- O O9057f514442ca720550ed7157ed08eaa

Abstract O
biodiversity B-climate-organisms
in O
the O
Tropical O
Andes O
is O
under O
continuous O
threat O
from O
anthropogenic O
activities O
. O

We O
modeled O
a O
broad O
range O
of O
taxa O
( O
11,012 O
species O
of O
birds B-climate-organisms
and O
vascular B-climate-organisms
plants I-climate-organisms
) O
, O
including O
both O
endemic B-climate-organisms
and O
widespread O
species B-climate-organisms
and O
provide O
a O
comprehensive O
estimation O
of O
climate O
change O
impacts O
on O
the O
Andes O
. O

While O
some O
areas O
appear O
to O
be O
climatically O
stable B-climate-properties
( O
e.g. O
Pichincha O
and O
Imbabura O
in O
Ecuador O
; O
and O
Narino O
, O
Cauca O
, O
Valle O
del O
Cauca O
and O
Putumayo O
in O
Colombia O
) O
and O
hence O
depict O
little O
diversity B-climate-hazards
loss I-climate-hazards
and/or O
potential O
species O
gains O
, O
major O
negative O
impacts O
were O
also O
observed O
. O

Tropical B-climate-nature
high O
Andean O
grasslands B-climate-nature
( O
paramos B-climate-nature
and O
punas B-climate-nature
) O
and O
evergreen B-climate-nature
montane I-climate-nature
forests I-climate-nature
, O
two O
key O
ecosystems O
for O
the O
provision O
of O
environmental O
services O
in O
the O
region O
, O
are O
projected O
to O
experience O
negative O
changes O
in O
species O
richness O
and O
high O
rates O
of O
species O
turnover O
. O

-DOCSTART- -X- O Oed7cd9a511deddaadd13e3af6655675d

Every O
year O
hundreds O
of O
thousands O
of O
people B-climate-assets
become O
homeless B-climate-impacts
due O
to O
natural O
disasters O
and O
are O
consequently O
in O
need O
of O
temporary O
accommodation O
until O
they O
can O
return O
to O
their O
reconstructed O
homes B-climate-assets
. O

An O
example O
of O
this O
is O
the O
Pakistan O
earthquake B-climate-hazards
in O
2005 O
where O
no O
appropriate O
winterised B-climate-mitigations
tents I-climate-mitigations
were O
available O
and O
thermal O
comfort O
could O
not O
be O
gained O
, O
given O
the O
extremely O
cold O
winter O
conditions O
. O

In O
its O
second O
part O
the O
work O
responds O
to O
the O
pressing O
need O
for O
the O
winterisation B-climate-mitigations
of O
family O
tents O
by O
presenting O
a O
number O
of O
different O
options O
for O
a O
floor O
insulation B-climate-mitigations
. O

-DOCSTART- -X- O Oc7e5398796e30f616a36d2c48d496b95

This O
study O
considers O
the O
Pan B-climate-models
- I-climate-models
European I-climate-models
Soil I-climate-models
Erosion I-climate-models
Risk I-climate-models
Assessment I-climate-models
- O
Desertification B-climate-models
Mitigation I-climate-models
Cost I-climate-models
- I-climate-models
Effectiveness I-climate-models
modelling O
approach O
to O
capture O
a O
greater O
range O
of O
climatic O
conditions O
to O
assess O
the O
potential O
effect O
of O
an O
improved O
agricultural B-climate-mitigations
management I-climate-mitigations
practice I-climate-mitigations
emerged O
from O
field O
trials O
as O
a O
promising O
strategy O
for O
enhancing O
food B-climate-assets
security I-climate-assets
and O
reducing O
soil B-climate-hazards
and I-climate-hazards
land I-climate-hazards
degradation I-climate-hazards
. O

The O
model O
considers O
the O
biophysical O
and O
socio O
- O
economic O
benefits O
of O
the O
improved O
soil B-climate-mitigations
conservation I-climate-mitigations
technique I-climate-mitigations
( O
T3 O
) O
- O
residue B-climate-mitigations
mulch I-climate-mitigations
combined O
with O
pigeon B-climate-mitigations
pea I-climate-mitigations
hedges I-climate-mitigations
and O
an O
organic O
amendment O
, O
against O
a O
local O
baseline O
practice O
( O
T0 O
) O
. O

-DOCSTART- -X- O Ocb6cfc0153e7593e9dd9d77c8b532b08

Wind B-climate-mitigations
energy I-climate-mitigations
is O
associated O
with O
many O
geographical O
factors O
including O
wind B-climate-properties
speed I-climate-properties
, O
climate O
change O
, O
surface B-climate-nature
topography I-climate-nature
, O
environmental O
impacts O
, O
and O
several O
economic O
factors O
, O
most O
notably O
the O
advancement O
of O
wind B-climate-mitigations
technology I-climate-mitigations
and O
energy B-climate-properties
prices I-climate-properties
. O

Wind B-climate-mitigations
energy I-climate-mitigations
generation O
is O
directly O
related O
to O
the O
characteristics O
of O
spatial O
wind B-climate-nature
. O

In O
Kuwait O
, O
wind B-climate-mitigations
energy I-climate-mitigations
is O
an O
appropriate O
choice O
as O
a O
source O
of O
energy O
generation O
. O

Climatic O
data O
were O
attained O
through O
the O
readings O
of O
eight O
distributed O
monitoring O
stations O
affiliated O
with O
Kuwait B-climate-organizations
Institute I-climate-organizations
for I-climate-organizations
Scientific I-climate-organizations
Research I-climate-organizations
( O
KISR B-climate-organizations
) O
. O

The O
researchers O
applied O
the O
Suitability B-climate-models
Model I-climate-models
to O
analyze O
the O
study O
by O
using O
the O
ArcGIS B-climate-models
program O
. O

-DOCSTART- -X- O O75f3b5bbbbe464e1edefdde3d37077f4

To O
compare O
the O
fitness O
of O
five O
stochastic O
differential O
equations O
( O
SDEs O
) O
to O
the O
European B-climate-organizations
Union I-climate-organizations
allowances O
spot O
price O
, O
we O
apply O
regression O
theory O
to O
obtain O
the O
point O
and O
interval O
estimations O
for O
the O
parameters O
of O
the O
SDEs O
. O

An O
empirical O
evaluation O
demonstrates O
that O
the O
mean O
reverting O
square O
root O
process O
( O
MRSRP O
) O
has O
the O
best O
fitness O
of O
five O
SDEs O
to O
forecast O
the O
spot O
price O
. O

To O
reduce O
the O
degree O
of O
smog B-climate-nature
, O
we O
develop O
a O
new O
trading B-climate-mitigations
scheme I-climate-mitigations
in O
which O
firms O
have O
to O
hand O
many O
more O
allowances O
to O
the O
government O
when O
they O
emit O
one O
unit O
of O
air B-climate-hazards
pollution I-climate-hazards
on O
heavy O
pollution B-climate-hazards
days O
, O
versus O
one O
allowance O
on O
clean O
days O
. O

Thus O
, O
we O
set O
up O
the O
SDE O
MRSRP O
model O
with O
Markovian O
switching O
to O
analyse O
the O
evolution O
of O
the O
spot O
price O
in O
such O
a O
scheme O
. O

-DOCSTART- -X- O Ode7dfd0d31a3dd297aa778527f48929d

In O
a O
large O
scale O
, O
forest B-climate-nature
productivity O
is O
primarily O
driven O
by O
two O
large O
fluxes O
, O
gross B-climate-properties
primary I-climate-properties
production I-climate-properties
( O
GPP B-climate-properties
) O
, O
which O
is O
the O
source O
for O
all O
carbon O
in O
forest B-climate-nature
ecosystems O
, O
and O
heterotrophic B-climate-properties
respiration I-climate-properties
. O

Here O
we O
show O
how O
uncertainty O
of O
GPP B-climate-properties
projections O
of O
Finnish O
boreal B-climate-nature
forests I-climate-nature
divides O
between O
input O
, O
mechanistic O
and O
parametric O
uncertainty O
. O

We O
used O
the O
simple O
semi O
- O
empirical O
stand O
GPP B-climate-properties
and O
water B-climate-nature
balance I-climate-nature
model O
PRELES B-climate-models
with O
an O
ensemble O
of O
downscaled O
global O
circulation O
model O
( O
GCM O
) O
projections O
for O
the O
21st O
century O
under O
different O
emissions O
and O
forcing O
scenarios O
( O
both O
RCP O
and O
SRES O
) O
. O

-DOCSTART- -X- O O11a48067ce25ab1e08b9865cb2337130

The O
dynamic O
changes O
of O
forest B-climate-hazards
fire I-climate-hazards
events O
are O
due O
to O
the O
swing O
of O
climate O
parameter O
. O

Geospatial O
technology O
has O
strong O
capability O
to O
analyze O
various O
thematic O
datasets O
towards O
visualization O
of O
spatial O
/ O
temporal O
pattern O
and O
plays O
a O
vital O
role O
in O
fire B-climate-mitigations
management I-climate-mitigations
efforts O
. O

This O
paper O
aims O
to O
analyze O
the O
climate O
and O
forest B-climate-hazards
fire I-climate-hazards
trend O
using O
Geospatial O
technology O
in O
the O
state O
of O
Orissa O
of O
India O
. O

The O
84.5 O
% O
of O
forest B-climate-hazards
fire I-climate-hazards
events O
are O
observed O
in O
the O
month O
of O
March O
and O
April O
and O
it O
is O
significantly O
high O
in O
the O
south O
of O
Kandhamal O
, O
east O
of O
Kalahandi O
, O
north O
of O
Rayagada O
and O
north O
of O
Gajapati O
district O
. O

The O
solar B-climate-properties
radiation I-climate-properties
increased O
to O
144 O
% O
in O
the O
month O
of O
March O
when O
compared O
with O
preceding O
month O
whereas O
relative B-climate-properties
humidity I-climate-properties
was O
decreased O
to O
64 O
% O
in O
the O
same O
month O
. O

The O
evaluation O
of O
Cramer O
V O
coefficient O
values O
of O
minimum B-climate-properties
temperature I-climate-properties
, O
solar B-climate-properties
radiation I-climate-properties
, O
maximum B-climate-properties
temperature I-climate-properties
and O
relative B-climate-properties
humidity I-climate-properties
are O
found O
to O
be O
0.302 O
, O
0.327 O
, O
0.366 O
and O
0.482 O
respectively O
. O

-DOCSTART- -X- O O9fb2a34d46502970d8704fd344f3b7e5

study O
was O
set O
to O
investigate O
the O
impact O
of O
climate O
change O
on O
rural B-climate-assets
livelihoods I-climate-assets
in O
the O
North O
Nguu O
Mountains O
in O
Kilindi O
District O
, O
Tanzania O
. O

-DOCSTART- -X- O Oc2168e1bd2c3ba97a5864b542281b438

We O
combine O
these O
climate O
impact O
estimates O
with O
the O
GTAP B-climate-models
model O
of O
global B-climate-assets
trade I-climate-assets
in O
order O
to O
estimate O
the O
national B-climate-assets
welfare I-climate-assets
changes O
which O
are O
decomposed O
into O
three O
components O

-DOCSTART- -X- O Obfd3e2021e8c3ca8391aecc6350a8c38

Using O
mathematical O
modelling O
tools O
, O
we O
assessed O
the O
potential O
for O
land B-climate-problem-origins
use I-climate-problem-origins
change I-climate-problem-origins
( O
LUC B-climate-problem-origins
) O
associated O
with O
the O
Intergovernmental B-climate-organizations
Panel I-climate-organizations
on I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
low- O
and O
high O
- O
end O
emission B-climate-problem-origins
scenario O
( O
RCP2.6 B-climate-datasets
and O
RCP8.5 B-climate-datasets
) O
to O
impact O
malaria B-climate-impacts
transmission O
in O
Africa O
. O

To O
drive O
a O
spatially O
explicit O
, O
dynamical O
malaria B-climate-impacts
model O
, O
data O
from O
the O
four O
available O
earth O
system O
models O
( O
ESMs O
) O
that O
contributed O
to O
the O
LUC B-climate-problem-origins
experiment O
of O
the O
Fifth B-climate-models
Climate I-climate-models
Model I-climate-models
Intercomparison I-climate-models
Project I-climate-models
are O
used O
. O

-DOCSTART- -X- O Od047956e63de201932620ea0ef938b72

Statistically O
significant O
changes O
in O
tropopause B-climate-nature
fold I-climate-nature
frequencies O
are O
identified O
in O
both O
Hemispheres O
, O
occasionally O
exceeding O
3 O
% O
, O
which O
are O
associated O
with O
the O
projected O
changes O
in O
the O
position O
and O
intensity B-climate-properties
of O
the O
subtropical B-climate-nature
jet B-climate-nature
streams I-climate-nature
. O

A O
strengthening O
of O
ozone O
STT B-climate-nature
is O
projected O
for O
future O
at O
both O
Hemispheres O
, O
with O
an O
induced O
increase O
of O
transported O
stratospheric B-climate-nature
ozone O
tracer O
throughout O
the O
whole O
troposphere B-climate-nature
, O
reaching O
up O
to O
10 O
nmol O
/ O
mol O
in O
the O
upper O
troposphere B-climate-nature
, O
8 O
nmol O
/ O
mol O
in O
the O
middle O
troposphere B-climate-nature
and O
3 O
nmol O
/ O
mol O
near O
the O
surface B-climate-nature
. O

-DOCSTART- -X- O O2702c371a58dd1a390f788e3cd6c9240

Understanding O
the O
impact O
of O
various O
climate O
features O
on O
wave B-climate-nature
climate O
is O
important O
for O
effective O
coastal B-climate-nature
climate O
adaptation O
and O
mitigation B-climate-mitigations
strategy I-climate-mitigations
planning O
. O

In O
the O
present O
study O
, O
the O
effect O
of O
tropical B-climate-nature
and O
extra B-climate-nature
- I-climate-nature
tropical I-climate-nature
climate O
modes O
such O
as O
Indian B-climate-nature
Ocean I-climate-nature
Dipole I-climate-nature
( O
IOD B-climate-nature
) O
, O
El B-climate-nature
Niño I-climate-nature
Southern I-climate-nature
Oscillation I-climate-nature
( O
ENSO B-climate-nature
) O
and O
Southern B-climate-nature
Annular I-climate-nature
Mode I-climate-nature
( O
SAM B-climate-nature
) O
on O
wind B-climate-nature
- O
wave B-climate-nature
climate O
of O
the O
Indian O
Ocean O
( O
IO O
) O
is O
studied O
using O
multiple O
linear O
regression O
of O
individual O
climate O
indices O
on O
relevant O
wind B-climate-nature
- O
wave B-climate-nature
parameters O
. O

There O
are O
two O
regions O
of O
importance O
for O
swell B-climate-nature
generation O
in O
the O
Indian O
Ocean O
- O
a O
region O
between O
40 O
° O
and O
60 O
° O
S O
in O
the O
Southern O
Ocean O
( O
SO O
) O
and O
another O
region O
in O
the O
Eastern O
Tropical O
Indian O
Ocean O
( O
ETIO O
; O
10 O
° O
–30 O
° O
S O
, O
60 O
° O
–100 O
° O
E O
) O
. O

-DOCSTART- -X- O Oab16dc9a50f63c96ff6d0d58e3b146dc

As O
a O
result O
, O
state O
forestry B-climate-assets
authorities O
should O
take O
precautions O
against O
this O
bark B-climate-hazards
beetle I-climate-hazards
species O
in O
the O
pine B-climate-organisms
stands O
of O
northern O
Turkey O
in O
the O
future O
. O

-DOCSTART- -X- O O71aeba3d171884648fca991b73d2eba2

As O
global O
climate O
change O
alters O
many O
aspects O
of O
seasonal O
variability O
, O
including O
extreme O
events O
and O
changes O
in O
mean O
conditions O
, O
organisms B-climate-organisms
must O
respond O
appropriately O
or O
go O
extinct B-climate-hazards
. O

-DOCSTART- -X- O Oc0a6e55943b44425126281fbda78c9c4

my O
Erasmus O
year O
in O
La O
Coruna O
( O
Spain O
) O
, O
I O
was O
lucky O
enough O
to O
make O
friends O
with O
a O
person O
who O
has O
joined O
the O
humanitarian O
missions O
in O
the O
southern O
Philippines O
in O
support O
of O
the O
victims O
of O
Typhoon O
Bopha O
, O
who O
has O
instilled O
in O
me O
a O
great O
interest O
for O
the O
Filipino O
culture O
and O
their O
relationship O
with O
nature O
, O
the O
climate O
and O
natural O
disasters O
, O
and O
I O
promised O
myself O
to O
take O
part O
in O
humanitarian O
action O
once O
concluded O
the O
university O
studies O
. O

-DOCSTART- -X- O O1062ef7b930c8c5dca09e5e1ac12bdd3

Climate O
warming O
increases O
vulnerability O
to O
drought B-climate-hazards
in O
Mediterranean O
water O
- O
limited O
forests B-climate-nature
. O

However O
, O
we O
still O
lack O
knowledge O
of O
the O
long O
- O
term O
physiological O
responses O
of O
coexisting O
pine B-climate-organisms
species O
in O
these O
forests B-climate-nature
regarding O
their O
ability O
to O
cope O
with O
warming O
- O
induced O
drought B-climate-hazards
stress O
. O

-DOCSTART- -X- O O3af18f42ec58c39993cbbb5a444aaa6c

Transport B-climate-assets
infrastructure I-climate-assets
networks I-climate-assets
are O
increasingly O
vulnerable O
to O
disruption B-climate-impacts
from O
extreme B-climate-hazards
rainfall I-climate-hazards
events O
due O
to O
increasing O
surface B-climate-nature
water I-climate-nature
runoff I-climate-nature
from O
urbanization B-climate-problem-origins
and O
changes O
in O
climate O
. O

Impacts O
from O
such O
disruptions B-climate-impacts
typically O
extend O
far O
beyond O
the O
flood B-climate-impacts
footprint I-climate-impacts
, O
because O
of O
the O
interconnection O
and O
spatial O
extent O
of O
modern O
infrastructure B-climate-assets
. O

An O
integrated O
flood B-climate-hazards
risk O
assessment O
couples O
high O
resolution O
information O
on O
depth B-climate-properties
and O
velocity B-climate-properties
from O
the O
CityCAT B-climate-models
urban B-climate-hazards
flood I-climate-hazards
model O
with O
empirical O
analysis O
of O
vehicle B-climate-assets
speeds O
in O
different O
depths B-climate-properties
of I-climate-properties
flood I-climate-properties
water I-climate-properties
, O
to O
perturb O
a O
transport O
accessibility O
model O
and O
determine O
the O
impact O
of O
a O
given O
event O
on O
journey O
times O
across O
the O
urban O
area O
. O

A O
case O
study O
in O
Newcastle O
- O
upon O
- O
Tyne O
( O
UK O
) O
shows O
that O
even O
minor O
flooding B-climate-hazards
associate O
with O
a O
1 O
in O
10 O
year O
event O
can O
cause O
traffic B-climate-impacts
disruptions I-climate-impacts
of O
nearly O
half O
an O
hour O
. O

-DOCSTART- -X- O O8b951ff3cd22c0ae3bbed7effdeacc6d

Earth O
's O
atmospheric B-climate-properties
CO I-climate-properties
2 I-climate-properties
level I-climate-properties
has O
increased O
beyond O
400 O
ppm O
and O
still O
continuing O
to O
rise O
. O

In O
fact O
, O
this O
is O
the O
highest O
level O
in O
the O
last O
2 O
million O
years O
. O

One O
core O
strategies O
in O
the O
mitigation B-climate-mitigations
mix O
are O
negative O
emissions O
technologies O
( O
NETs O
) O
which O
are O
also O
explicitly O
described O
as O
important O
options O
in O
many O
IPCC B-climate-organizations
( O
AR5 B-climate-datasets
) O
CO B-climate-greenhouse-gases
2 I-climate-greenhouse-gases
emission B-climate-problem-origins
scenarios O
. O

Among O
others O
, O
BioEnergy O
with O
Carbon O
Capture O
and O
Storage O
( O
BECCS O
) O
are O
shown O
their O
potential O
for O
CO B-climate-greenhouse-gases
2 I-climate-greenhouse-gases
removable O
from O
the O
atmosphere B-climate-nature
. O

BECCS O
as O
the O
NETs O
are O
most O
widely O
selected O
by O
integrated O
assessment O
models O
( O
IAMs O
) O
to O
meet O
the O
requirements O
of O
temperature B-climate-properties
limits O
of O
2 O
° O
and O
below O
. O

-DOCSTART- -X- O O488090e1b7cc93816587443585c8aa54

The O
parameters O
that O
had O
a O
significant O
impact O
on O
shoreline B-climate-hazards
erosion I-climate-hazards
were O
: O
reef B-climate-nature
flat O
width B-climate-properties
, O
reef B-climate-nature
flat O
depth B-climate-properties
, O
island B-climate-nature
width B-climate-properties
, O
and O
atoll B-climate-nature
diameter B-climate-properties
. O

Atolls B-climate-nature
with O
narrower O
, O
deeper O
reef B-climate-nature
flats O
, O
narrower O
islands B-climate-nature
, O
and O
smaller O
diameters B-climate-properties
were O
most O
susceptible O
to O
shoreline B-climate-hazards
instability I-climate-hazards
with O
sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
. O

Windward B-climate-nature
islands I-climate-nature
are O
projected O
to O
lengthen O
and O
migrate O
toward O
the O
lagoon B-climate-nature
, O
leeward B-climate-nature
islands I-climate-nature
are O
projected O
to O
lengthen O
and O
migrate O
toward O
the O
reef B-climate-nature
rim O
, O
and O
oblique O
islands B-climate-nature
are O
projected O
to O
migrate O
leeward O
and O
toward O
the O
lagoon B-climate-nature
. O

-DOCSTART- -X- O O3d2f30d4b7abc7b927a634190241d89b

Global O
navigation O
satellite O
systems O
( O
GNSSs O
) O
have O
become O
an O
important O
tool O
to O
derive O
atmospheric B-climate-nature
products O
, O
such O
as O
the O
total O
zenith B-climate-properties
tropospheric I-climate-properties
delay I-climate-properties
( O
ZTD B-climate-properties
) O
and O
precipitable B-climate-properties
water I-climate-properties
vapor I-climate-properties
( O
PWV B-climate-properties
) O
for O
weather O
and O
climate O
studies O
. O

The O
ocean B-climate-properties
tide I-climate-properties
loading I-climate-properties
( O
OTL B-climate-properties
) O
effect O
is O
one O
of O
the O
primary O
errors O
that O
affects O
the O
accuracy O
of O
GNSS O
- O
derived O
ZTD B-climate-properties
/ O
PWV B-climate-properties
, O
which O
means O
the O
study O
and O
choice O
of O
the O
OTL B-climate-properties
model O
is O
an O
important O
issue O
for O
high O
- O
accuracy O
ZTD B-climate-properties
estimation O
. O

-DOCSTART- -X- O Obae06f77125e4e185730842d66e29a73

Mesoamerica O
and O
the O
Caribbean O
are O
low O
- O
latitude O
regions O
at O
risk O
for O
the O
effects O
of O
climate O
change O
. O

Global O
climate O
models O
provide O
large O
- O
scale O
assessment O
of O
climate O
drivers O
, O
but O
, O
at O
a O
horizontal O
resolution O
of O
100 O
km O
, O
can O
not O
resolve O
the O
effects O
of O
topography B-climate-nature
and O
land B-climate-properties
use I-climate-properties
as O
they O
impact O
the O
local O
temperature B-climate-properties
and O
precipitation B-climate-nature
that O
are O
keys O
to O
climate O
impacts O
. O

We O
developed O
a O
robust O
dynamical O
downscaling O
strategy O
that O
used O
the O
WRF B-climate-models
regional O
climate O
model O
to O
downscale O
at O
4 O
- O
12 O
km O
resolution O
GCM O
results O
. O

-DOCSTART- -X- O O >>> bpf Annotation of "everything" up to here

-DOCSTART- -X- O O >>> bpf Next a chronologic subselection

-DOCSTART- -X- O O26334055e107cf4a78da272c01811a7a

We O
use O
a O
combination O
of O
an O
integrated O
environmental O
model O
( O
IMAGE B-climate-models
) O
and O
climate O
envelope O
models O
for O
European O
plant B-climate-organisms
species I-climate-organisms
for O
several O
climate O
change O
scenarios O
to O
estimate O
changes O
in O
mean B-climate-properties
stable I-climate-properties
area I-climate-properties
of O
species B-climate-organisms
and O
species B-climate-organisms
turnover O
. O

-DOCSTART- -X- O O1258005cc451776c7081b32a38f671f8

-DOCSTART- -X- O Ode5713a54da8e93af5ecb699a2623c61

Our O
research O
is O
based O
on O
field O
data O
collected O
in O
the O
Northern O
Brazilian O
Amazon O
in O
2009 O
within O
the O
Small O
Grant O
research O
programme O
of O
the O
German B-climate-organizations
Federal I-climate-organizations
Ministry I-climate-organizations
for I-climate-organizations
Economic I-climate-organizations
Cooperation I-climate-organizations
and I-climate-organizations
Development I-climate-organizations
( O
BMZ B-climate-organizations
) O
. O

Based O
on O
these O
data O
, O
we O
show O
how O
dynamic O
discrete O
time O
models O
can O
be O
developed O
and O
implemented O
using O
the O
dynamic O
simulation O
software O
STELLA B-climate-models
. O

-DOCSTART- -X- O Oc8a1087a5ebab8abee2e609aa884aa63

One O
of O
the O
ways O
to O
reducing O
greenhouse O
gas O
emission O
is O
by O
assessing O
the O
environmental O
impact O
associated O
with O
food B-climate-problem-origins
production I-climate-problem-origins
, O
and O
one O
of O
the O
well O
- O
known O
methodologies O
used O
for O
environmental O
impact O
evaluation O
is O
life O
cycle O
assessment O
model O
( O
LCA O
) O
. O

This O
paper O
presents O
the O
results O
of O
LCA O
analysis O
of O
cassava B-climate-assets
flour O
production O
in O
Southwestern O
Nigeria O
. O

-DOCSTART- -X- O O248fbab473537819c48428ef707953eb

In O
the O
semiarid B-climate-nature
interior O
of O
the O
Iberian O
Peninsula O
, O
the O
topographic O
insulation B-climate-mitigations
from O
the O
surrounding O
seas B-climate-nature
promotes O
the O
role O
of O
internal O
sources O
of O
moisture B-climate-properties
and O
water B-climate-mitigations
recycling I-climate-mitigations
in O
the O
rainfall B-climate-nature
regime O
. O

In O
inland O
Iberia O
, O
the O
annual O
cycle O
of O
precipitation B-climate-nature
often O
has O
a O
distinctive O
peak O
in O
the O
springtime O
, O
when O
evapotranspiration B-climate-nature
( O
ET B-climate-nature
) O
is O
the O
highest O
, O
in O
contrast O
to O
the O
coastal B-climate-nature
areas I-climate-nature
, O
where O
it O
is O
more O
closely O
related O
to O
the O
external O
moisture B-climate-properties
availability O
and O
synoptic O
forcing O
, O
with O
a O
maximum O
in O
winter O
- O
autumn O
and O
a O
pronounced O
minimum O
in O
the O
summer O
. O

-DOCSTART- -X- O O8b5ffca44eacad5306caf10322dbdd3b

In O
large O
mountainous B-climate-nature
catchments B-climate-nature
, O
shallow O
unconfined O
alluvial B-climate-nature
aquifers B-climate-nature
play O
an O
important O
role O
in O
conveying O
subsurface B-climate-nature
runoff I-climate-nature
to O
the O
foreland O
. O

Here O
, O
an O
approach O
to O
overcome O
this O
discrepancy O
is O
discussed O
using O
the O
example O
of O
the O
German O
- O
Austrian O
Upper O
Danube O
catchment O
, O
where O
a O
coarse O
ground B-climate-nature
water I-climate-nature
flow O
model O
was O
developed O
using O
MODFLOW B-climate-models
. O

In O
order O
to O
show O
the O
efficiency O
of O
the O
developed O
method O
, O
it O
was O
tested O
and O
compared O
to O
a O
finely O
discretized O
ground B-climate-nature
water I-climate-nature
model O
of O
the O
Ammer O
subcatchment O
. O

-DOCSTART- -X- O Oc66519cefc848756ba96da04af61061c

Smallholder B-climate-assets
farmers B-climate-assets
produce O
about O
70 O
% O
of O
Africa O
’s O
food B-climate-assets
supply I-climate-assets
. O

These O
farmers B-climate-assets
are O
vulnerable O
to O
a O
number O
of O
risks O
, O
mainly O
climate O
related O
, O
which O
have O
a O
tremendous O
impact O
on O
food B-climate-assets
security I-climate-assets
and O
thus O
poverty B-climate-impacts
. O

This O
paper O
describes O
crops B-climate-assets
, O
water B-climate-nature
and O
drought B-climate-hazards
services O
that O
are O
being O
developed O
in O
the O
AfriCultuReS B-climate-organizations
project O
. O

-DOCSTART- -X- O O08b7c23a0b109945fb9d1204dc36b0b2

Urmia O
Lake O
, O
the O
largest O
lake B-climate-nature
in O
Iran O
, O
is O
an O
important O
water B-climate-nature
body O
and O
habitat B-climate-organisms
for O
a O
variety O
of O
different O
species B-climate-organisms
. O

Subsequently O
these O
values O
were O
introduced O
to O
the O
Long B-climate-models
Ashton I-climate-models
Research I-climate-models
Station I-climate-models
Weather I-climate-models
Generator I-climate-models
model O
( O
LARS B-climate-models
- I-climate-models
WG I-climate-models
) O
to O
downscale O
and O
produce O
time O
series O
of O
temperature B-climate-properties
and O
precipitation B-climate-nature
in O
the O
future O
, O
subject O
to O
the O
uncertainty O
of O
climate O
models O
. O

-DOCSTART- -X- O Ob8a294ed94386b38464f9e122ee2e91d

LDAS B-climate-models
- I-climate-models
Monde I-climate-models
ingests O
satellite O
- O
derived O
surface B-climate-properties
soil I-climate-properties
moisture I-climate-properties
( O
SSM B-climate-properties
) O
and O
leaf B-climate-properties
area I-climate-properties
index I-climate-properties
( O
LAI B-climate-properties
) O
estimates O
to O
constrain O
the O
interactions O
between O
soil B-climate-nature
, O
biosphere B-climate-nature
, O
and O
atmosphere B-climate-nature
( O
ISBA B-climate-models
) O
land B-climate-nature
surface I-climate-nature
model O
( O
LSM O
) O
coupled O
with O
the O
CNRM B-climate-organizations
( O
Centre B-climate-organizations
National I-climate-organizations
de I-climate-organizations
Recherches I-climate-organizations
Météorologiques I-climate-organizations
) O
version O
of O
the O
total B-climate-models
runoff I-climate-models
integrating I-climate-models
pathways I-climate-models
( O
CTRIP B-climate-models
) O
continental O
hydrological B-climate-nature
system O
( O
ISBA B-climate-models
- I-climate-models
CTRIP I-climate-models
) O
. O

LDAS B-climate-models
- I-climate-models
Monde I-climate-models
is O
forced O
by O
the O
ERA-5 B-climate-models
atmospheric B-climate-nature
reanalysis O
from O
the O
European B-climate-organizations
Center I-climate-organizations
for I-climate-organizations
Medium I-climate-organizations
Range I-climate-organizations
Weather I-climate-organizations
Forecast I-climate-organizations
( O
ECMWF B-climate-organizations
) O
from O
2010 O
to O
2016 O
leading O
to O
a O
seven O
- O
year O
, O
quarter O
degree O
spatial O
resolution O
offline O
reanalysis O
of O
land B-climate-nature
surface I-climate-nature
variables O
( O
LSVs O
) O
over O
CONUS O
. O

-DOCSTART- -X- O O8986db15c45f03fde7360524ad9dce0a

Over O
Arctic O
sea B-climate-nature
ice I-climate-nature
, O
pressure B-climate-properties
ridges O
and O
floe B-climate-nature
and O
melt B-climate-nature
pond I-climate-nature
edges O
all O
introduce O
discrete O
obstructions O
to O
the O
flow O
of O
air B-climate-nature
or O
water B-climate-nature
past O
the O
ice B-climate-nature
and O
are O
a O
source O
of O
form B-climate-properties
drag I-climate-properties
. O

The O
drag B-climate-nature
coefficients O
are O
incorporated O
into O
the O
Los B-climate-models
Alamos I-climate-models
Sea I-climate-models
Ice I-climate-models
Model I-climate-models
( O
CICE B-climate-models
) O
and O
show O
the O
influence O
of O
the O
new O
drag B-climate-nature
parameterization O
on O
the O
motion O
and O
state O
of O
the O
ice B-climate-nature
cover I-climate-nature
, O
with O
the O
most O
noticeable O
being O
a O
depletion O
of O
sea B-climate-nature
ice I-climate-nature
over O
the O
west O
boundary O
of O
the O
Arctic O
Ocean O
and O
over O
the O
Beaufort O
Sea O
. O

-DOCSTART- -X- O O3770f1a827feac49cfbecead9eceecd2

Water B-climate-hazards
scarcity I-climate-hazards
affects O
large O
parts O
of O
the O
world O
. O

Despite O
recent O
studies O
that O
analyze O
the O
effect O
of O
climate O
change O
on O
water B-climate-hazards
scarcity I-climate-hazards
, O
e.g. O
using O
climate O
projections O
under O
representative O
concentration O
pathways O
( O
RCP O
) O
of O
the O
fifth B-climate-datasets
assessment I-climate-datasets
report I-climate-datasets
of O
the O
IPCC B-climate-organizations
( O
AR5 B-climate-datasets
) O
, O
decision O
support O
for O
closing O
the O
water B-climate-hazards
gap I-climate-hazards
between O
now O
and O
2100 O
does O
not O
exist O
at O
a O
meaningful O
scale O
and O
with O
a O
global O
coverage O
. O

Water B-climate-assets
supply I-climate-assets
was O
computed O
using O
the O
PCR B-climate-models
- I-climate-models
GLOBWB I-climate-models
hydrological B-climate-nature
and O
water B-climate-assets
resources I-climate-assets
model O
, O
parameterized O
at O
5 O
arcminutes O
for O
the O
whole O
globe O
. O

We O
ran O
PCR B-climate-models
- I-climate-models
GLOBWB I-climate-models
with O
a O
daily O
forcing O
derived O
from O
five O
different O
GCM O
models O
from O
the O
CMIP5 B-climate-models
( O
GFDL B-climate-models
- I-climate-models
ESM2 I-climate-models
M I-climate-models
, O
Hadgem2 B-climate-models
- I-climate-models
ES I-climate-models
, O
IPSL B-climate-models
- I-climate-models
CMA5 I-climate-models
- I-climate-models
LR I-climate-models
, O
MIROC B-climate-models
- I-climate-models
ESM I-climate-models
- I-climate-models
CHEM I-climate-models
, O
NorESM1 B-climate-models
- I-climate-models
M I-climate-models
) O
that O
were O
bias O
corrected O
using O
observation O
- O
based O
WATCH B-climate-datasets
data O
between O
1960 O
- O
1999 O
. O

-DOCSTART- -X- O O569437d00ead7080f5c75040e171931c

SEAREG B-climate-organizations
analyses O
socio O
- O
economic O
and O
environmental O
effects O
of O
climate O
and O
sea B-climate-properties
level I-climate-properties
changes I-climate-properties
in O
the O
Baltic O
Sea O
Region O
( O
BSR O
) O
. O

Within O
the O
project O
, O
the O
Swedish B-climate-organizations
Meteorological I-climate-organizations
and I-climate-organizations
Hydrological I-climate-organizations
Institute I-climate-organizations
( O
SMHI B-climate-organizations
) O
develops O
scenarios O
of O
future O
climate O
and O
sea B-climate-nature
level I-climate-nature
for O
the O
year O
2100 O
which O
will O
then O
be O
connected O
with O
regional O
data O
. O

Coastal B-climate-nature
dynamics O
was O
estimated O
for O
the O
next O
100 O
years O
, O
considering O
the O
island O
of O
Usedom O
as O
an O
example O
. O

Data O
of O
the O
different O
SMHI B-climate-organizations
climate O
scenarios O
were O
used O
for O
this O
calculation O
. O

-DOCSTART- -X- O Oee2139dbea88f78d6f98b2eb6225939b

By O
comparing O
the O
predicted O
fire B-climate-hazards
PM2.5 B-climate-hazards
emissions I-climate-hazards
from O
the O
interpretable O
ML O
model O
with O
the O
Global B-climate-datasets
Fire I-climate-datasets
Emissions I-climate-datasets
Database I-climate-datasets
( O
GFED B-climate-datasets
) O
observations O
and O
predictions O
from O
process O
- O
based O
models O
in O
the O
Fire B-climate-models
Modeling I-climate-models
Intercomparison I-climate-models
Project I-climate-models
( O
FireMIP B-climate-models
) O
, O
the O
ML O
model O
is O
also O
used O
to O
diagnose O
the O
process O
- O
based O
models O
to O
inform O
future O
development O
. O

Results O
show O
promising O
performance O
for O
the O
ML O
model O
, O
Community B-climate-models
Land I-climate-models
Model I-climate-models
( O
CLM B-climate-models
) O
, O
and O
Joint B-climate-models
UK I-climate-models
Land I-climate-models
environment I-climate-models
Simulator I-climate-models
- I-climate-models
Interactive I-climate-models
Fire I-climate-models
And I-climate-models
Emission I-climate-models
Algorithm I-climate-models
For I-climate-models
Natural I-climate-models
Environments I-climate-models
( O
JULES B-climate-models
- I-climate-models
INFERNO I-climate-models
) O
in O
reproducing O
the O
spatial O
distributions O
, O
seasonality O
, O
and O
interannual O
variability O
of O
fire B-climate-hazards
emissions I-climate-hazards
over O
CONUS O
. O

-DOCSTART- -X- O O2339c8dabadce81d70167959a015f8d5

In O
the O
present O
study O
, O
the O
dynamical O
downscaling O
technique O
was O
applied O
in O
the O
Advanced B-climate-models
Weather I-climate-models
Research I-climate-models
and I-climate-models
Forecasting I-climate-models
numerical O
model O
WRF B-climate-models
- I-climate-models
ARW I-climate-models
, O
to O
investigate O
and O
validate O
the O
performance O
of O
different O
physics O
parameterizations O
. O

The O
WRF B-climate-models
model O
, O
was O
forced O
by O
ERA B-climate-datasets
- I-climate-datasets
INTERIM I-climate-datasets
reanalysis O
data O
, O
for O
a O
short O
period O
of O
one O
year O
( O
January O
2002 O
– O
December O
2002 O
) O
, O
over O
the O
area O
of O
the O
MED B-climate-models
- I-climate-models
CORDEX I-climate-models
domain O
of O
20 O
km O
horizontal O
resolution O
, O
downscaled O
to O
the O
domain O
of O
Greece O
with O
grid O
spacing O
of O
5 O
km O
. O

The O
results O
of O
the O
model O
simulations O
have O
been O
compared O
with O
all O
available O
station O
measurements O
from O
the O
European O
Climate B-climate-datasets
Assessment I-climate-datasets
and I-climate-datasets
Dataset I-climate-datasets
( O
ECA&D B-climate-datasets
) O
for O
the O
daily B-climate-properties
precipitation I-climate-properties
and O
2 O
- O
m O
air B-climate-properties
temperature I-climate-properties
, O
through O
the O
computation O
of O
statistical O
metrics O
. O

-DOCSTART- -X- O Oe3dc4c0ede0ce8da36307bc1e0650485

Halogenated O
organic O
compounds O
are O
naturally O
produced O
in O
the O
ocean B-climate-nature
and O
emitted O
to O
the O
atmosphere B-climate-nature
. O

The O
halogenated O
very O
short O
- O
lived O
substances O
( O
VSLS O
) O
, O
such O
as O
bromoform O
, O
have O
atmospheric B-climate-properties
lifetimes I-climate-properties
of O
less O
than O
half O
a O
year O
. O

When O
VSLS O
reach O
the O
stratosphere B-climate-nature
, O
they O
enhance O
ozone B-climate-hazards
depletion I-climate-hazards
and O
thus O
impact O
the O
climate O
. O

During O
the O
research B-climate-observations
cruises I-climate-observations
SO234 I-climate-observations
- I-climate-observations
2 I-climate-observations
and O
SO235 B-climate-observations
in O
July O
- O
August O
2014 O
onboard O
RV B-climate-observations
SONNE I-climate-observations
, O
we O
measured O
oceanic B-climate-properties
and I-climate-properties
atmospheric I-climate-properties
concentrations I-climate-properties
of O
bromoform O
( O
tropical B-climate-properties
lifetime I-climate-properties
at O
10 O
km O
= O
17 O
days O
) O
, O
dibromomethane O
( O
150 O
days O
) O
and O
methyl O
iodide O
( O
3.5 O
days O
) O
in O
the O
subtropical B-climate-nature
and O
tropical B-climate-nature
West O
Indian O
Ocean O
and O
calculated O
their O
emission O
strengths O
. O

We O
use O
the O
Langrangian O
transport O
model O
FLEXPART I-climate-models
driven O
by O
ERA B-climate-datasets
- I-climate-datasets
Interim I-climate-datasets
meteorological O
fields O
to O
investigate O
the O
transport O
of O
oceanic B-climate-nature
emissions I-climate-nature
in O
the O
atmosphere B-climate-nature
. O

Furthermore O
, O
we O
investigate O
the O
connection O
between O
the O
Asian B-climate-nature
monsoon I-climate-nature
anticyclone I-climate-nature
and O
the O
oceanic B-climate-nature
source O
regions O
using O
backward O
trajectories O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/85808f0cb1cb020d8ad0c40822992dc6cdd371ca

This O
paper O
presents O
a O
new O
approach O
to O
long O
- O
term O
flood B-climate-hazards
risk O
assessment O
in O
accordance O
with O
the O
UK B-climate-datasets
Foresight I-climate-datasets
Future I-climate-datasets
Flooding I-climate-datasets
Report I-climate-datasets

-DOCSTART- -X- O O https://semanticscholar.org/paper/8c19cb08726b1111a44034d5129c56e13804616b

Quantifying O
the O
reliability O
of O
precipitation B-climate-properties
datasets O
for O
monitoring O
large‐scale O
East O
Asian O
precipitation B-climate-properties
variations O
. O

Early O
detection O
of O
extreme O
drought B-climate-hazards
and O
flood B-climate-hazards
events O
either O
over O
the O
whole O
globe O
or O
a O
broad O
geographical O
region O
, O
and O
timely O
dissemination O
of O
this O
information O
, O
is O
indispensable O
for O
mitigation B-climate-mitigations
and O
disaster B-climate-mitigations
preparedness I-climate-mitigations
. O

Recently O
, O
the O
APEC B-climate-organizations
Climate I-climate-organizations
Center I-climate-organizations
( O
APCC B-climate-organizations
) O
has O
launched O
a O
global O
precipitation B-climate-properties
variation O
monitoring O
product O
based O
on O
the O
Climate B-climate-datasets
Anomaly I-climate-datasets
Monitoring I-climate-datasets
System I-climate-datasets
- I-climate-datasets
Outgoing I-climate-datasets
Longwave I-climate-datasets
Radiation I-climate-datasets
Precipitation I-climate-datasets
Index I-climate-datasets
( O
CAMS B-climate-datasets
- I-climate-datasets
OPI I-climate-datasets
) O
data O
. O

Here O
we O
quantify O
the O
reliability O
of O
CAMS B-climate-datasets
- I-climate-datasets
OPI I-climate-datasets
, O
as O
well O
as O
other O
gauge O
- O
satellite O
- O
merged O
and O
reanalysis O
precipitation B-climate-properties
datasets O
, O
for O
the O
purpose O
of O
monitoring O
large O
- O
scale O
precipitation B-climate-properties
variability O
in O
East O
Asia O
. O

The O
ground O
truth O
is O
the O
newly O
available O
gauge O
- O
based O
data O
from O
the O
project O
titled O
' O
Asian B-climate-datasets
Precipitation I-climate-datasets
- I-climate-datasets
Highly I-climate-datasets
- I-climate-datasets
Resolved I-climate-datasets
Observational I-climate-datasets
Data I-climate-datasets
Integration I-climate-datasets
Towards I-climate-datasets
Evaluation I-climate-datasets
( O
APHRODITE B-climate-datasets
) O
of O
the O
Water B-climate-nature
Resources I-climate-nature
' O
. O

It O
is O
found O
that O
the O
seasonal O
- O
to O
- O
interannual O
rainfall B-climate-nature
deficit O
and O
surplus O
given O
by O
various O
reanalysis O
systems O
sometimes O
do O
not O
match O
the O
spatial O
patterns O
seen O
in O
the O
APHRODITE B-climate-datasets
data O
. O

Moreover O
, O
maps O
showing O
the O
Standardized B-climate-properties
Precipitation I-climate-properties
Index I-climate-properties
( O
SPI B-climate-properties
) O
become O
less O
and O
less O
reliable O
as O
the O
time O
scale O
based O
on O
which O
values O
are O
calculated O
increases O
. O

Overall O
, O
CAMS B-climate-datasets
- I-climate-datasets
OPI I-climate-datasets
is O
found O
to O
be O
reliable O
for O
monitoring O
large O
- O
scale O
precipitation B-climate-properties
variations O
over O
the O
East O
Asian O
sector O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/4c34681d4a74dfb6e02ec9d7e28bfaf70d27c000

Current O
and O
Future O
Impacts O
of O
Extreme O
Flood B-climate-hazards
Events O
. O

Inadequate B-climate-problem-origins
timber I-climate-problem-origins
extraction I-climate-problem-origins
management O
of O
forests B-climate-nature
, O
cattle B-climate-problem-origins
farming O
, O
abusive B-climate-problem-origins
recreational I-climate-problem-origins
practices I-climate-problem-origins
, O
and O
rapid O
urban B-climate-problem-origins
expansion I-climate-problem-origins
are O
all O
factors O
that O
create O
significant O
problems O
in O
the O
Cantabrian O
area O
watershed B-climate-nature
for O
the O
sustainable O
management O
of O
the O
hydrological B-climate-nature
ecosystem O
services O
. O

In O
this O
chapter O
, O
ENSEMBLES B-climate-organizations
RT3 O
climate O
model O
outputs O
are O
analysed O
and O
calibrated O
with O
local O
observation O
data O
recorded O
daily O
. O

The O
hydrological O
/ O
hydraulic O
coupling O
model O
( O
MikeShe B-climate-models
- O
Mike11 B-climate-models
) O
is O
applied O
by O
forcing O
the O
validated O
model O
output O
. O

According O
to O
the O
results O
, O
under O
the O
medium O
greenhouse O
emission B-climate-problem-origins
scenario O
( O
A1B B-climate-datasets
) O
, O
the O
Regional O
Climate O
Models O
HIRHAM I-climate-models
( O
2001–2050 O
) O
and O
RACMO B-climate-models
( O
2051–2100 O
) O
show O
an O
increase O
in O
extreme B-climate-hazards
precipitation I-climate-hazards
. O

The O
expected O
changes O
show O
spatial O
variability O
depending O
on O
local O
characteristics O
( O
topography B-climate-nature
, O
proximity O
to O
the O
coast B-climate-nature
, O
vegetation B-climate-nature
, O
etc O
. O
) O
and O
ranging O
between O
6–26 O
% O
for O
HIRHAM B-climate-models
and O
11–12 O
% O
for O
RACMO B-climate-models
. O

An O
increase O
of O
22 O
± O
2 O
% O
is O
expected O
in O
the O
HIRHAM B-climate-models
climatic O
model O
for O
upstream O
peak O
discharge B-climate-properties
with O
a O
return O
period O
exceeding O
50 O
years O
. O