Influences of Climate Change and Its Interannual Variability on ...

Influences of Climate Change and Its Interannual Variability on ...


ong>Influencesong> ong>ofong> ong>Climateong> ong>Changeong> ong>andong> ong>Itsong> ong>Interannualong> ong>Variabilityong>

on Surface Energy Fluxes from 1948 to 2000

SHENG Li ∗1,2 (DZ ), LIU Shuhua 1 (Ù), ong>andong> Heping LIU 2

1 Department ong>ofong> Atmospheric Sciences, School ong>ofong> Physics, Peking University, Beijing 100871

2 Department ong>ofong> Physics, Atmospheric Sciences, ong>andong> Geoscience,

Jackson State University, Jackson, MS, U.S.A.

(Received 6 December 2009òrevised 23 Feruary 2010)


Understong>andong>ing changes in long>andong> surface processes over the past several decades requires knowledge ong>ofong>

trends ong>andong> interannual variability in surface energy fluxes in response to climate change. In our study, the

Community Long>andong> Model version 3.5 (CLM3.5), driven by the latest updated hybrid reanalysis-observational

surface climate data from Princeton University, is used to obtain global distributions ong>ofong> surface energy fluxes

during 1948 to 2000. Based on the climate data ong>andong> simulation results, long-term trends ong>andong> interannual

variability (IAV) ong>ofong> both climatic variables ong>andong> surface energy fluxes for this span ong>ofong> 50+ years are derived

ong>andong> analyzed. Regions with strong long-term trends ong>andong> large IAV for both climatic variables ong>andong> surface

energy fluxes are identified. These analyses reveal seasonal variations in the spatial patterns ong>ofong> climate ong>andong>

surface fluxes; however, spatial patterns in trends ong>andong> IAV for surface energy fluxes over the past ∼50 years

do not fully correspond to those for climatic variables, indicating complex responses ong>ofong> long>andong> surfaces to

changes in the climatic forcings.

Key words: climate change, surface energy fluxes, trends, interannual variability

Citation: Sheng, L., S. H. Liu, ong>andong> H. P. Liu, 2010: ong>Influencesong> ong>ofong> climate change ong>andong> its interannual

variability on the surface energy fluxes from 1948 to 2000. Adv. Atmos. Sci., 27(6), 1438–1452, doi:


1. Introduction

Global climate has changed significantly in the past

century, ong>andong> it is projected to continue to evolve in the

21st century (e.g., IPCC, 2007). ong>Climateong> change has

large impacts on changes in long>andong> surface characteristics.

For instance, snow cover has declined in the past

decades as a result ong>ofong> climate warming (e.g., Brown,

2000). Soil moisture regimes have changed with a declining

trend since 1975 in response to changes in precipitation

patterns (e.g., Fan ong>andong> Dool, 2004). Satellite

remote sensing data indicate enhanced vegetation

growth (i.e., increased greenness) in the northern high

latitudes as a result ong>ofong> a lengthening ong>ofong> growing seasons

(Myneni et al., 1997). For a long-term perspective, climate

change leads to changes in ecosystem functioning

ong>andong> structure (Field et al., 2007). However, it remains

unclear as to how climate-induced changes in long>andong> sur-

face characteristics feed back on changes in climatic

variables through changes in long>andong> surface fluxes.

Long>andong> surfaces have direct effects on climate

through biophysical processes, which alter the transfer

ong>ofong> heat, moisture, ong>andong> momentum between long>andong>

ong>andong> the atmosphere, ong>andong> biogeochemical processes,

which alter the chemical composition ong>ofong> the atmosphere

through changes in exchange ong>ofong> trace gases

(e.g., Bonan, 2002; Feddema et al., 2005). Exchange

ong>ofong> sensible heat between long>andong> ong>andong> the atmosphere provides

heating ong>ofong> the boundary layer ong>andong> thus leads to

changes in air temperature there. The exchange ong>ofong>

latent heat between long>andong> ong>andong> atmosphere affects the

amount ong>ofong> water vapor in the boundary layer ong>andong> thus

has large a impact on the amount ong>ofong> clouds ong>andong> precipitation

(Bonan, 2002). Under this circumstance,

climate-induced changes in long>andong> surface characteristics

provide significant feedbacks to climate through

∗ Corresponding author: SHENG Li,

© China National Committee for International Association ong>ofong> Meteorology ong>andong> Atmospheric Sciences (IAMAS), Institute ong>ofong> Atmospheric

Physics (IAP) ong>andong> Science Press ong>andong> Springer-Verlag Berlin Heidelberg 2010

NO. 6 SHENG ET AL. 1439

changes in surface energy fluxes.

Previous studies have suggested that considerable

changes have occurred in surface energy fluxes ong>andong>

components ong>ofong> the hydrological budget (e.g., evapotranspiration

ong>andong> runong>ofong>f) in response to climateinduced

changes in atmospheric forcing ong>andong> in long>andong>

surface characteristics for the past half-century (see review

by Huntington, 2006). Since there are no direct,

long-term observations ong>ofong> long>andong> surface fluxes for evaluating

trends, changes in long>andong> surface processes can be

assessed, for example, by the decline in pan evaporation

rates in many regions ong>ofong> the world since the 1950s

(e.g., Peterson et al., 1995; Ohmura ong>andong> Wild, 2002).

Various mechanisms have been proposed to explain

links between declining pan evaporation levels ong>andong> actual

evaporation (e.g., Peterson et al., 1995; Brutsaert

ong>andong> Parlange, 1998; Ohmura ong>andong> Wild, 2002). Some

studies believe that the decline in pan evaporation is

attributable to decreased solar irradiance (Peterson

et al., 1995; Roderick ong>andong> Farquhar, 2004). However,

other studies suggest that the increased water

vapor concentration as a result ong>ofong> warming-induced increases

in evaporation to the atmosphere may have depressed

pan evaporation readings (Brutsaert ong>andong> Parlange,

1998; Golubev et al., 2001). Despite the debate

about the relationship between pan evaporation

ong>andong> realized evaporative fluxes, many analysts believe

that the global hydrological cycle has accelerated in

the past decades ong>andong> has varied in both intensity ong>andong>

spatial pattern (e.g., Walter et al., 2004; Stocker ong>andong>

Raible, 2005; IPCC, 2007).

Due to incomplete observations ong>ofong> long>andong> surface

fluxes, simulations using comprehensive long>andong> surface

models ong>andong> historical atmospheric forcing data are

useful tools for studying spatial patterns ong>andong> temporal

variations in long>andong> surface fluxes for regional ong>andong> even

global perspectives (e.g., Qian et al., 2007). A regional

study using the Community Long>andong> Model (CLM3.0) indicates

an increasing trend in terrestrial evapotranspiration

ong>andong> a decreasing trend in sensible heat fluxes

over the Mississippi River basin from 1948 to 2004

(Qian et al., 2007). These regional trends are believed

to be attributable to a decline in net radiation as a result

ong>ofong> the increasing cloudiness as well as wetter soil

conditions due to the increasing basin-averaged precipitation,

although this effect is partly compensated by

an increase in runong>ofong>f (Qian et al., 2007). In addition,

large spatial variations in surface energy fluxes ong>andong> the

components ong>ofong> hydrological budgets in the Mississippi

River basin ong>andong> the contiguous United States (Qian et

al., 2007) demonstrate different responses ong>ofong> long>andong> surfaces

to changes in large-scale atmospheric forcings.

It remains unclear, however, as to how different long>andong>

surfaces have respond to trends ong>andong> interannual vari-

ability (IAV) in climatic forcings over the past decades

through long>andong>-atmosphere interactions on regional or

even global scales.

In this study, we identify regions with strong

changes in climatic variables ong>andong> surface energy fluxes

through identification ong>ofong> their long-term trends ong>andong>

IAV from 1948 to 2000. The relationships between

trends/IAV in surface energy fluxes ong>andong> trends/IAV in

climatic variables are also analyzed. The surface climatic

variables are directly derived from the latest updated

climate dataset, while the surface energy fluxes

are derived from comprehensive long>andong>-surface model

simulations (i.e., using the National Center for Atmospheric

Research Community Long>andong> Model version 3.5;

CLM3.5) from 1948 to 2000. The objective ong>ofong> this

study is to study how long>andong> surfaces respond differently

to climate trends ong>andong> its interannual variability over

the past ∼50 years.

2. Methodology

CLM3.5, which is used to simulate components ong>ofong>

the surface energy budget in this study, is a latest version

ong>ofong> the Community Long>andong> Model (CLM) ong>andong> is well

documented in Oleson et al. (2004, 2008). In CLM3.5,

the spatial heterogeneity ong>ofong> long>andong> surfaces is represented

by a nested sub-grid hierarchy in which each grid cell

is composed ong>ofong> multiple long>andong> units. Each long>andong> unit can

have a different number ong>ofong> snow/soil columns, ong>andong> each

column has multiple plant functional types (PFTs).

As an advanced process-based long>andong>-surface model,

CLM has been extensively evaluated by ong>ofong>fline comparisons

with surface fluxes directly measured by eddy

covariance systems in FLUXNET (It is a global flux

network, which is composed ong>ofong> many regional networks

from around the world: CarboeuropeIP, AmeriFlux,

Fluxnet-Canada, LBA, Asiaflux, Chinaflux, USCCC,

Ozflux, Carboafrica, Kong>ofong>lux, NECC, TCOS-Siberia

ong>andong> Afriflux. e.g., Stöckli et al., 2008), ong>ofong>fline simulations

on catchment-scale climate-hydrology interactions

(e.g., Qian et al., 2007), ong>andong> coupled experiments

(e.g., Dickinson et al., 2006), indicating consistently

good performance in reproducing long>andong> surface fluxes.

All these studies indicate that CLM is able to produce

reliable long>andong> surface fluxes, although it has deficiencies

in some regions. Driven by the Princeton climate data

set (Sheffield et al., 2006), our simulations are conducted

globally at a resolution ong>ofong> T85 (approximately

1.41 ◦ ×1.41 ◦ ), with rectilinear latitude/longitude grids

that utilize a nested sub-grid hierarchy to represent

the spatial heterogeneity ong>ofong> long>andong> surfaces.

The Princeton climate data set, with 3-hourly temporal

resolution ong>andong> 1 ◦ ×1 ◦ spatial resolution, was constructed

by combining a suite ong>ofong> global observation


ong>andong> reanalysis datasets, including the National Centers

for Environmental Prediction-National Center for

Atmospheric Research (NCEP-NCAR) reanalysis, Climatic

Research Unit (CRU) monthly climate variables,

Global Precipitation Climatology Project (GPCP)

daily precipitation, Tropical Rainfall Measuring Mission

(TRMM) 3-hourly precipitation, ong>andong> National

Aeronautics ong>andong> Space Administration (NASA) Langley

monthly surface radiation budget data (Sheffield

et al., 2006). In this data set, precipitation corrections

based on observations were made to reduce known biases

in the reanalysis that have been shown to exert

erroneous impacts on modeled long>andong> surface energy ong>andong>

water budgets. Additionally, several critical adjustments

ong>andong> updates were made to reduce regional errors

ong>andong> uncertainties (J. Sheffield, 2008, personal communication).

We use the 1948 data to repeat simulations for 18

years as a spin-up, ong>andong> we then continue the simulations

from 1948 to 2000 for our analysis. The spinup

procedure in this study ensures that the deep soil

moisture at 1.78 m reaches equilibrium. The spin-up

duration in our study is longer than in Cosgrove et al.

(2003) in which it takes less than 7 years for the wet

soils ong>andong> longer than 11 years for dry soils to reach a

0.01% cutong>ofong>f-based equilibrium in North America.

In our study, we quantify long-term trends ong>andong>

IAV for both climatic variables ong>andong> the surface energy

fluxes from 1948 to 2000, including temperature

(T ), precipitation (P ), vapor pressure deficit (VPD),

net radiation (Rn), sensible heat flux (H), latent heat

flux (LE), ong>andong> soil moisture (Sm) from the surface to

a depth ong>ofong> 0.166 m. Trends are taken as the slopes

ong>ofong> time-series variables in each grid from 1948 to 2000

ong>andong> a linear t-test with a 10% significant level is used

to ensure statistical significance. IAV is defined as the

stong>andong>ard deviation after de-trending in each grid box.

Except for temperature ong>andong> albedo, IAV for all other

variables is normalized by the corresponding 53-year

mean for each grid box. This normalization removes

the dependence ong>ofong> stong>andong>ard deviations on their corresponding


3. Results

3.1 Global trends

Figures 1a ong>andong> 1b were directly produced from

the Princeton climate data (1948–2000) ong>andong> show the

trends for T ,VPD,ong>andong>Pover the global long>andong> areas

(excluding Antarctica). Both T ong>andong> VPD show

increasing trends at rates ong>ofong> 0.086◦C per decade ong>andong>

1.08 Pa per decade from 1948 to 2000, respectively,

while the P trend is statistically insignificant during

this period with a weak downward trend ong>ofong> about 4.08

mm per decade (Table 1). The temperature trend here

is within the ranges reported in IPCC (2007). For example,

trends for T obtained from different long>andong>-based

observational datasets range from 0.068 ◦ C±0.024 ◦ Cto

0.084 ◦ C±0.021 ◦ C per decade for the period from 1901

to 2005 (Brohan et al., 2006; Smith ong>andong> Reynolds,

2005; Hansen et al., 2001; Lugina et al., 2005)

ong>andong> from 0.188 ◦ C±0.069 ◦ C to 0.315 ◦ C±0.088 ◦ Cper

decade from 1979 to 2005. However, trends for T obtained

from the ERA-40 reanalysis are quite low in

comparison (i.e., 0.03 ◦ C ong>andong> 0.07 ◦ C per decade for

the northern ong>andong> southern hemispheres, respectively)

(Jones ong>andong> Moberg, 2003). Though the uncertainty

in the precipitation trend is larger than that for T ,

given the heterogeneity ong>ofong> precipitation ong>andong> the limited

observations, the P trend is still consistent with

that reported in other studies. Based on a blend ong>ofong>

remote sensing ong>andong> gauge datasets, for example, a decline

in precipitation is reported with a range from

−6.63±5.18 mm to −0.38±3.89 mm per decade over

long>andong> from 1951 to 2000 (Peterson, ong>andong> Vose; Chen et

al., 2002; Adler et al., 2003; Mitchell ong>andong> Jones, 2005),

while other authors indicate an increasing trend with a

rate ong>ofong> 1.62±5.32 mm per decade (Rudolf et al., 1994).

Rn ong>andong> H have decreasing trends over the global

long>andong> areas with rates ong>ofong> −0.40 W m −2 per decade ong>andong>

−0.45 W m −2 per decade, respectively. The declining

trend for Rn obtained from our simulations from 1948

to 2000 is comparable to that reported (i.e., −1.5 W

m −2 per decade) by the IPCC, whose estimate does

not consider the influence ong>ofong> natural forcings (e.g., the

surface radiative forcing from volcanic eruptions provides

an important contribution during the past 25

years) (IPCC, 2007; Nozawa et al., 2005; Takemura et

al., 2005). Wild et al. (2004) estimate that the surface

net radiation decreased at a rate ong>ofong> between −0.1

ong>andong> −2.8 Wm −2 per decade from 1960 to 1990, based

primarily on observations over the northern extratropical

long>andong> areas. It is noted that the decreasing trend

for H is well correlated with that for Rn (Fig. 1).

Although Rn has large impacts on changes on H, itis

likely that it does not directly determine the LE trend,

which demonstrates that other factors (e.g., soil water

availability) may have stronger influences on LE than

the available energy, especially in dry regions (IPCC,


Though LE shows a weak increasing trend from

1950 to 2000 (0.05 W m −2 per decade), it had an increasing

trend before 1972 ong>andong> a weak decreasing trend

after 1975. Globally, the increase during the overall

period studied here is related to the increasing trend

in atmospheric moisture demong>andong> (i.e., the increased

VPD as shown in Fig. 1a) ong>andong> intensified turbulent

mixing (i.e., via the mean wind speed, not shown).

NO. 6 SHENG ET AL. 1441

Fig. 1. Globally averaged trends in (a) temperature (T , ◦ C per decade) ong>andong>

VPD (Pa per decade); (b) precipitation (P ,mmd −1 per decade); (c) the surface

energy budget (W m −2 per decade, Rn: net radiation, H: sensible heat

flux, ong>andong> LE: latent heat flux; ong>andong> (d) soil moisture (Sm, from the surface to

the depth ong>ofong> 0.166 m, m 3 m −3 per decade), excluding Antarctica.

Table 1. Statistical results for climatic variables ong>andong> surface energy fluxes. All variables are averaged over global long>andong>

areas excluding Antarctica. Here, R is the regression coefficient.


Slope per 0.086 ◦ C 4.08 mm 1.08 Pa −0.40 W m −2 −0.45 W m −2

0.05 W m −2


R 0.57 0.05 0.41 0.78 0. 79 0.25 0.36

360.26 Pa 75.99 W m −2

42.18 W m −2

32.98 W m −2

Mean 6.50 ◦ C 1.97 mm d −1

−19.5 cm 3 m −3

0.41 mm 3 mm −3


3.2 Spatial patterns ong>ofong> trends ong>andong> interannual

variability in climatic variables

3.2.1 Trends in climatic variables

Figure 2 shows the seasonal distributions ong>ofong> linear

trends in T , P , ong>andong> VPD (spring: March, April,

ong>andong> May; summer: June, July, ong>andong> August; autumn:

September, October, ong>andong> November; ong>andong> winter: December,

January, ong>andong> February). All trends with statistical

significance at a level ong>ofong> 10% are marked by

white dots. Large increasing temperature trends with

a rate ong>ofong> more than 0.6◦C per decade occur mainly

in high-latitude regions ong>ofong> the Northern Hemisphere

(NH), particularly in central Asia ong>andong> the high- ong>andong>

mid-latitudes ong>ofong> North America. The strongest trends

are found in winter ong>andong> spring, followed by autumn

ong>andong> summer.

The decreasing trends for precipitation occur in

the Sahara desert ong>andong> Arabian Peninsula especially in

summer, autumn, ong>andong> winter ong>andong> another strong decreasing

trend occurs in tropical Africa for all seasons.

Observations from both western ong>andong> eastern Africa

prove that there are decreasing trends ong>ofong> P in these

regions (e.g., Le Barbe et al., 2002). The clear increasing

trends exist over the Iranian plateau for winter ong>andong>

spring, as well as in broad mid-latitude regions ong>ofong> Asia

ong>andong> southern South America (for spring, autumn, ong>andong>


Generally, VPD has increasing trends in the high

latitudes ong>andong> weak decreasing trends in mid- ong>andong>

low- latitudes, though some increasing trends are also

present in areas ong>ofong> Africa ong>andong> Australia. Our results

indicate that the increasing trends ong>ofong> VPD in high latitudes

are associated more with the increasing saturation

vapor pressure due to the increasing temperature

than changes in vapor content, while the decreasing

trends in mid- to low- latitudes are associated more

with the increasing vapor content than temperatureinduced

changes in saturation vapor pressure (IPCC,

2007; Robinson, 2000; Wang ong>andong> Gaffen, 2001).

3.2.2 ong>Interannualong> variability in climatic variables

The stong>andong>ard deviations ong>ofong> the 53-year time-series

variables (i.e., the climate variables discussed in this

section or components ong>ofong> the surface energy flux budget

discussed in the next section) reflect the strengths

ong>ofong> the interannual variability. The larger the stong>andong>ard

deviation, the stronger the interannual variability for

such a variable. In general, the strong IAV ong>ofong> T is primarily

located most prominently in the high latitudes

ong>ofong> North America (i.e., the areas to north ong>ofong> 50◦N), such as Greenlong>andong>, northern Europe, central Siberia,

ong>andong> central Asia. These areas show variations ong>ofong> more

than 2◦C in winter, followed in interannual variability

amplitude by spring, autumn, ong>andong> summer (Figs.


The strongest IAV ong>ofong> P occurs in the central USA

(in spring, autumn, ong>andong> winter), western USA (in summer

ong>andong> autumn), Mexico (in winter), southern South

America (in all seasons), west Asia (in spring, summer,

ong>andong> autumn), ong>andong> India (in winter) (Figs. 3e–3h).

Secondary areas ong>ofong> high IAV areas for precipitation exist

in the Sahara desert (in all seasons), tropical Africa

(except in spring), ong>andong> southern Africa (in spring ong>andong>

summer). It is interesting to note that the IAV ong>ofong> P at

high latitudes is very small for all seasons, indicating

small interannual variability in these regions.

The regions ong>ofong> highest IAV ong>ofong> VPD are located

in northern North America, Central Asia, ong>andong> western

Australia, with the interannual stong>andong>ard deviation

varying spatially in different seasons (Figs. 3i–3l).

Note that the IAV ong>ofong> VPD is not directly correlated

to the IAV ong>ofong> P (Figs. 3i–3l vs. Figs. 3e–3h). Our

analysis indicates that the IAV ong>ofong> VPD is determined

primarily by the IAV ong>ofong> T in the high latitudes ong>andong>

by the IAV ong>ofong> water vapor pressure in the low to mid


3.3 Spatial patterns ong>ofong> trends ong>andong> interannual

variability in surface energy fluxes

3.3.1 Trends in the surface energy fluxes

The long>andong>-atmosphere system is a coupled system

through long>andong> surface processes. It is expected

that changes in climatic variables (i.e., climatic forcings)

will lead to changes in the surface energy fluxes

through long>andong>-atmosphere interactions. What are the

consequences in terms ong>ofong> responses ong>ofong> long>andong> surfaces

to changes in trends ong>andong>/or interannual variability in

climatic variables? To answer this question requires

quantifying trends ong>andong> interannual variability for each

component ong>ofong> the surface energy budget ong>andong> analyzing

possible links between trends/IAV for climatic variables

ong>andong> trends/IAV for components ong>ofong> the surface

energy budget.

In general, Rn shows widespread decreasing trends

covering most ong>ofong> the global long>andong> areas in all seasons

with strong signatures in autumn ong>andong> winter ong>andong> weak

ones in spring ong>andong> summer (Fig. 4). Large decreasing

trends (approximately −0.25 W m −2 per decade)

occur over northern Eurasia ong>andong> North America in autumn

(excluding Alaska) (Fig. 4c) ong>andong> over the lowto

mid-latitudes ong>ofong> North America ong>andong> Eurasia in winter

(Fig. 4d). Some increasing trends in Rn are also

present primarily in central South America, central

South Africa, ong>andong> northern Australia for all seasons,

though some small centers are also found for different

seasons. It is interesting to note that these decreasing/increasing

trends in Rn actually correspond well

to the increasing/decreasing trends in net longwave

NO. 6 SHENG ET AL. 1443

Fig. 2. Global distributions ong>ofong> trends in T ( ◦ C per decade) for (a) spring (MAM), (b) summer

(JJA), (c) autumn (SOD), ong>andong> (d) winter (DJF); P (mm per decade) for (e) spring, (f) summer,

(g) autumn, ong>andong> (h) winter; ong>andong> VPD (Pa per decade) for (i) spring, (j) summer, (k) autumn, ong>andong>

(l) winter. These trends are directly derived from the Princeton climate data for each grid box

(180 × 360 grid boxes globally) for the period from 1948 to 2000. Trends exceeding a significance

level ong>ofong> 10% are marked by white dots.


Fig. 3. Global distributions ong>ofong> interannual variability (IAV) described by stong>andong>ard derivations for

T ( ◦ C; a: spring, b: summer, c: autumn, d: winter) ong>andong> normalized IAV (NIAV) for P (e: spring,

f: summer, g: autumn, h: winter) ong>andong> VPD (i: spring, j: summer, k: autumn, l: winter). NIAV is

the IAV normalized by its 53-year mean for each grid.

NO. 6 SHENG ET AL. 1445

Fig. 4. Global distributions ong>ofong> trends in Rn (W m −2 per decade, downward is positive;

a: spring, b: summer, c: autumn, d: winter), H (W m −2 per decade; e: spring,

f: summer, g, autumn, h, winter), LE (W m −2 per decade; i: spring, j: summer, k:

autumn, l: winter), ong>andong> Sm (m −3 m −3 per decade; m: spring, n: summer, o: autumn,

p: winter). All trends are obtained based on CLM3.5 simulations with 128 × 256 grid

boxes globally from 1948 to 2000. Trends exceeding a significance level ong>ofong> 10% are

marked by white dots.


radiation for all seasons (Fig. 5). As shown in Fig.

5, however, contributions from changes in net shortwave

radiation to changes in Rn are relatively small

as compared with those from net longwave radiation

(Fig. 5). In fact, albedo has a decreasing trend in the

northern high latitudes particularly in spring ong>andong> winter,

followed by autumn ong>andong> summer. This decreasing

trend in albedo alone actually leads to an increase in

net shortwave radiation over these regions (Figs. 5e–

5h). Our results indicate that the spatial patterns ong>ofong>

trends in net shortwave radiation shown in Figs. 5e–

5h are due to the combined consequence ong>ofong> changes in

albedo ong>andong> changes in incoming shortwave radiation

as a result ong>ofong> changes in cloudiness.

For a longer-term perspective, changes in net radiation

are reflected by a different partitioning ong>ofong> available

energy into sensible ong>andong> latent heat fluxes over

different long>andong>scapes. This energy partitioning is primarily

controlled by ecosystem functioning through

stomatal activities that alter latent heat flux ong>andong> thus

the Bowen ratio over vegetated surfaces. Our results

indicate that the spatial patterns for the trends ong>ofong> H

correspond quite well to those ong>ofong> Rn, with slight differences

in the magnitudes ong>ofong> those trends (Figs. 4e–4h).

Moreover, the magnitudes ong>ofong> trends in H are quite

close to those in Rn, suggesting that changes in Rn

are primarily balanced by changes in H.

As indicated in Figs. 4i–4l, LE experiences increased

trends in western North America, southern

South America, the northern Eurasian continent, central

Africa, western Australia, ong>andong> some regions in

southern Asia for all seasons with variations in the

magnitudes ong>ofong> those trends. In low latitudes from

northern Africa to Asia, the widespread decreases in

the LE trends are present for all seasons. The decreasing

trends in LE are also found in central South

America, southern South Africa, ong>andong> eastern Australia.

Our analysis indicates that spatial patterns

for the LE trends are associated with those for the

soil moisture trends (Figs. 4m–4p). A decrease in

soil moisture availability depresses evapotranspiration,

while an increase in soil moisture availability favors

evapotranspiration. For a long-term perspective, our

results suggest that terrestrial ecosystems are considerably

conservative in terms ong>ofong> their response to trend

changes in climatic variables.

3.3.2 ong>Interannualong> variability in surface energy fluxes

The seasonal patterns ong>ofong> IAV in the components

ong>ofong> the surface energy fluxes after normalization are illustrated

in Fig. 6. One striking feature for IAV in

Rn is the three zonal bong>andong>s in different regions for

different seasons: the Arctic regions in spring (Fig.

6a), the high-latitude regions around 55 ◦ N including

North America ong>andong> Eurasia in autumn (Fig. 6c), ong>andong>

the mid-latitude regions around 45 ◦ Nong>ofong>NorthAmerica

ong>andong> Eurasia in winter (Fig. 6d). However, there

are no obvious regions with high IAV ong>ofong> Rn in summer

(Fig. 6b). Actually, our simulations indicate that

the three zones with high IAV in Rn are the combined

consequence ong>ofong> climatic variability ong>andong> the feedback

between snow-albedo ong>andong> the atmosphere. In spring,

the high IAV ong>ofong> Rn in the Arctic (Fig. 6a) is related to

the interannual variability in snow-cover that is caused

by the interannual variability in air temperature, leading

to the high interannual variability in the surface

albedo ong>andong> longwave radiation ong>andong>, in turn, Rn in this

region (Fig. 7). However, the contribution ong>ofong> IAV in

longwave radiation to IAV in Rn is small as compared

with that ong>ofong> IAV in albedo to IAV in Rn. In summer

(Fig. 6b), however, IAV ong>ofong> Rn is small due to the small

interannual variability in climatic variables ong>andong> long>andong>

surface characteristics, resulting in only small impacts

on the long>andong> surface albedo, net shortwave radiation,

ong>andong> net longwave radiation for this season (Fig. 7). In

autumn (Fig. 6c), the high IAV in Rn in the mid to

highlatitude regions around 55 ◦ N are likely associated

with interannual variability in snow events (ong>andong> thus

albedo) ong>andong> the net longwave radiation. In winter, the

high IAV ong>ofong> Rn in mid-latitude regions extends southward

(to around 45 ◦ N in Fig. 6d) as compared with

the patterns in spring ong>andong> autumn, correspond primarily

to the interannual variability in spatial extent

ong>ofong> snow cover, leading to the interannual variability in

albedo that has been noted over the past few decades

(e.g., Brown, 2000; Dye, 2002; Chapin et al., 2005; Euskirchen

et al., 2006). Our calculations suggest that

other climate factors that affect the longwave radiation

budget have minor contribution in magnitude to

the IAV in Rn.

Net radiation (i.e., Rn) absorbed by the long>andong>surface

is mainly balanced by H ong>andong> LE. However,

the energy partitioning ong>ofong> Rn into H ong>andong> LE varies

greatly over different long>andong> surfaces with different soil

moisture conditions ong>andong> vegetation properties. One

striking feature is that the spatial patterns ong>ofong> IVA in

H correspond well to those in Rn, indicating that variations

in H are strongly dependent upon on variations

in Rn. However, the spatial patterns ong>ofong> IAV in LE do

not fully correspond to those ong>ofong> Rn, indicating that

variations in Rn have small impacts on variations in

LE. On the other hong>andong>, the relationships between variations

in Rn ong>andong> H as well as those in Rn ong>andong> LE

demonstrate the complexity ong>ofong> coupling ong>ofong> the long>andong>atmosphere


Spatially, high IAV for H in spring occurs along

the northeast coast ong>ofong> Canada ong>andong> the northern coast

ong>ofong> the Eurasian continent, corresponding to the Arctic

NO. 6 SHENG ET AL. 1447

Fig. 5. Global distributions ong>ofong> trends in albedo (% per decade; a: spring, b: summer, c: autumn,

d: winter), net shortwave radiation (Rs, Wm −2 per decade, downward is positive; e: spring, f:

summer, g: autumn, h: winter), ong>andong> net longwave radiation (R1, Wm −2 per decade, upward is

positive; i: spring, j: summer, k: autumn, l: winter). All trends are obtained based on CLM3.5

simulations with 128 × 256 grid boxes globally from 1948 to 2000. Trends exceeding a significance

level ong>ofong> 10% are marked by white dots.


Fig. 6. Global distributions ong>ofong> normalized interannual variability (NIAV) described by stong>andong>ard

derivations/mean for Rn (a: spring, b: summer, c: autumn, d: winter), H (e: spring, f: summer,

g, autumn, h: winter), LE (i: spring, j: summer, k: autumn, l: winter), ong>andong> Sm (m: spring, n:

summer, o: autumn, p: winter). NIAV is the IAV normalized by its 53-year mean for each grid.

NO. 6 SHENG ET AL. 1449

Fig. 7. Global distributions ong>ofong> interannual variability (IAV) described by stong>andong>ard derivations for

albedo (a: spring, b: summer, c: autumn, d: winter); ong>andong> normalized IAV (NIAV) for net shortwave

radiation (e: spring, f: summer, g: autumn, h: winter) ong>andong> net longwave radiation (i: spring, j:

summer, k: autumn, l: winter). NIAV is the IAV normalized by its 53-year mean for each grid.


IAV zonal bong>andong> for Rn (Figs. 6a ong>andong> 6e). In autumn,

both H ong>andong> Rn have bong>andong>s ong>ofong> large IAV around

55 ◦ N in North America (excluding Alaska) ong>andong> Eurasia

(Figs. 6c ong>andong> 6g). In winter, the zonal bong>andong>s

ong>ofong> high IAV for H are located in the regions around

45 ◦ N, with increased extent pushing southward into

the United States as compared with the pattern for

Rn (Figs. 6d ong>andong> 6h). In all seasons, some spots ong>ofong>

high IAV in H are present over central South America,

part ong>ofong> Africa, ong>andong> part ong>ofong> Australia (Figs. 6f).

Note that the spatial patterns ong>ofong> IAV in LE are

more complicated than those in H in terms ong>ofong> their

correspondence with Rn. Generally, LE not only depends

on the available energy, atmospheric moisture

demong>andong> (i.e., VPD), ong>andong> turbulent mixing capacity,

but also on the availability ong>ofong> soil moisture, which

has large impacts on stomatal functioning (i.e., stomatal

conductance). Our analysis indicates that the soil

moisture is the main factor in determining LE or its

variability in drier regions. For instance, the high LE

IAV over western Mexico (in spring, autumn, ong>andong> winter),

northern Africa including the Arabian Peninsula

(in all seasons), ong>andong> southern Asia (in spring ong>andong> winter)

is likely to be associated with the large Sm IAV

over these regions (Figs. 6i–6l vs. Figs. 6m–6p). The

high LE IAV occurring over Australia for all seasons is

also highly related to the high IAV in Sm there (Figs.

6i–6l vs. Figs. 3e–3h ong>andong> Figs. 6m–6p).

4. Discussion ong>andong> conclusions

Quantifying the surface energy budget as well as

its temporal variations ong>andong> interannual variability allows

us to better understong>andong> how the long>andong>-surface responds

to climate change in different regions. Though

FLUXNET provides the most intensive measurements

ong>ofong> long>andong> surface fluxes available, using over 400 eddy

covariance towers, these measurements only represent

fluxes from the scales ong>ofong> the tower footprints up to

a few kilometers (,

2008; Baldocchi et al., 2001; Running et al., 1999).

To obtain the surface energy budget on global scales

requires scaling up these direct flux measurements by

combining such data with other approaches (e.g., remote

sensing) ong>andong>/or process-based models (Xiao et

al., 2008). These up-scaled fluxes are, however, limited

to short durations due to the availabilities ong>ofong> the

FLUXNET data ong>andong> satellite remote sensing data. To

study long-term patterns (e.g., 53 years in this study)

ong>ofong> long>andong> surface fluxes, we have to rely on simulations

ong>ofong> long>andong> surface models. In this study, we used CLM3.5

for this purpose. Since CLM3.5 has been extensively

validated ong>andong> verified over different long>andong> surfaces ong>andong>

different regions, we are confident that this model is

reliable in reproducing long>andong> surface fluxes. In addition,

the atmospheric forcing data that are used to

drive CLM3.5 are the product ong>ofong> direct observation

ong>andong> reanalysis. The long>andong> surface fluxes produced from

CLM3.5 should be able to capture major characteristics

ong>ofong> long>andong>-atmosphere interactions including spatialtemporal

variations. It is realized that, however, uncertainties

in modeling results are inevitable due to

uncertainties in parameterization ong>ofong> physical, biophysical,

biogeochemical processes in treating long>andong> surface

processes ong>andong> also due to biases in the reanalysis data.

Nevertheless, it should be stressed that uncertainties

in the modeling results for this study should be largely

reduced since statistical quantities (e.g., trends ong>andong>

stong>andong>ard deviations) instead ong>ofong> absolute magnitudes

ong>ofong> long>andong> surface fluxes are discussed based on the analysis

ong>ofong> a long time series ong>ofong> data (i.e., 53 years).

We have identified some regions with strong trends

ong>andong> interannual variability in climatic forcing variables,

as well as in the surface energy fluxes (Rn,

H, ong>andong> LE). Our results indicate that since long>andong>atmospheric

interactions are non-linearly coupled processes,

trends ong>andong> interannual variability in the surface

energy fluxes in the past 53 years are not necessarily

fully correlated with those in climatic forcing variables

(e.g., temperature, precipitation, ong>andong> VPD).

In general, Rn shows decreasing trends worldwide

with varying rates in different regions. The decreased

trends in H are well correlated with those for Rn. The

decreased trends for LE are not correlated with any

one particular kinds ong>ofong> forcing, but are likely the consequences

ong>ofong> multiple factors.

ong>Interannualong> variability in Rn is observed ong>andong> is

believed to be associated strongly with variations in

snow-albedo feedbacks ong>andong> warming at different latitudes

for different seasons. Spatial distributions ong>ofong>

IAV in H have good correspondence with those in Rn.

However, spatial distributions ong>ofong> interannual variability

in LE do not correlate fully with those in climatic

forcings ong>andong>/or Rn, indicating complex responses ong>ofong>

long>andong> surfaces to climatic forcings.

Long>andong> surfaces influence the atmosphere through

long>andong> surface fluxes. Sensible heat flux determines

boundary layer heating (thus influencing air temperature),

while latent heat flux affects boundary layer

moistening (thus influencing moisture, clouds, ong>andong>

precipitation). Therefore, the increasing (decreasing)

trends in the surface fluxes tend to increase (decrease)

air temperature ong>andong> moisture, thus affecting weather

on short-term time scales ong>andong> climate on long-term

time scales. Additionally, the high IAV in long>andong> surface

fluxes could cause more severe/extreme weather activity

(e.g., summer heat waves, convective precipitation

events) on short-term time scales ong>andong> have significant

NO. 6 SHENG ET AL. 1451

contributions to strong seasonal or interannual variability

in climate on long-term time scales. Therefore,

regions with strong trends ong>andong> IAV in surface energy

fluxes should be given more attention regarding their

roles in weather ong>andong> climate change.

Acknowledgements. We are grateful for Dr. Justin

Sheffield at Princeton University for his support in data

sharing ong>andong> discussions. This work was supported in

part by the National Basic Research Program ong>ofong> China

(973 program; 2009CB421402) ong>andong> the NOAA Center

for Atmospheric Sciences (NCAS) at Howard University

(NA06OAR4810172). The first author acknowledges the

China Scholarship Council for partial financial support for

her dissertation research project at Jackson State University.


Adler, R. F., ong>andong> Coauthors, 2003: The version 2 Global

Precipitation Climatology Project (GPCP) monthly

precipitation analysis (1979–present). J. Hydrometeorol.,

4, 1147–1167.

Baldocchi, D., ong>andong> Coauthors, 2001: FLUXNET: A new

tool to study the temporal ong>andong> spatial variability

ong>ofong> ecosystem-scale carbon dioxide, water vapor, ong>andong>

energy flux densities. Bull. Amer. Meteor. Soc., 82,


Bonan, G. B., 2002: Ecological Climatology: Concepts

ong>andong> Applications. Cambridge University Press,


Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, ong>andong>

P. D. Jones, 2006: Uncertainty estimates in regional

ong>andong> global observed temperature changes: A new

dataset from 1850. J. Geophys. Res., 111, D12106,

doi: 10.1029/2005JD006548.

Brown, R. D., 2000: Northern Hemisphere snow cover

variability ong>andong> change, 1915–97. J. ong>Climateong>, 13,


Brutsaert, W., ong>andong> M. B. Parlange, 1998: Hydrologic cycle

explains the evaporation paradox. Nature, 396,

30, doi: 10.1038/23845.

Chapin III, F. S., ong>andong> Coauthors, 2005: Role ong>ofong> long>andong>surface

changes in Arctic summer warming. Science,

310, 657–660.

Chen, M., P. Xie, ong>andong> J. E. Janowiak, 2002: Global long>andong>

precipitation: A 50-yr monthly analysis based on

gauge observations. J. Hydrometeorol., 3, 249–266.

Cosgrove, B. A., ong>andong> Coauthors, 2003: Long>andong> surface

model spin-up behavior in the Northe American

Long>andong> Data Assimilation System (NLDAS). J. Geophys.

Res., 108, doi: 10.1029/2002JD003316.

Dickinson, R. E., ong>andong> Coauthors, 2006: The Community

Long>andong> Model ong>andong> its climate statistics as a component

ong>ofong> the Community ong>Climateong> System Model. J.

ong>Climateong>, 19, 2302–2324.

Dye, D. G., 2002: ong>Variabilityong> ong>andong> trends in the annual

snow-cover cycle in Northern Hemisphere long>andong> areas,

1972–2000. Hydrological Processes, 16, 3065–3077.

Euskirchen, E. S., A. D. McGuire, ong>andong> F. S. Chapin

III, 2006: Energy feedbacks ong>ofong> northern high-latitude

ecosystems to the climate system due to reduced

snow cover during 20th century warming. Global

ong>Changeong> Biology, 13, 2425–2438.

Fan, Y., ong>andong> H. V. D. Dool, 2004: ong>Climateong> prediction

center global monthly soil moisture data set at 0.5 ◦

resolution for 1948 to present. J. Geophys. Res., 109,

doi: 10.1029/2003JD004345.

Feddema, J. J., K. W. Oleson, G. B. Bonan, L. O. Mearns,

L. E. Buja, G. A. Meehl, ong>andong> W. M. Washingtong,

2005: The importance ong>ofong> long>andong>-cover change in simulating

future climates. Science, 310, 1674–1678.

Field, C. B., D. B. Lobell, H. A. Peters, ong>andong> N. R.

Chiariello, 2007: Feedbacks ong>ofong> terrestrial ecosystems

to climate change. Annual Reviews, 32, 1–29.

Golubev, V. S., J. H. Lawrimore, P. Y. Groisman, N.

A. Speranskaya, S. A. Zhuravin, M. J. Menne, T.

C. Peterson, ong>andong> R. W. Malone, 2001: Evaporation

changes over the contiguous United States ong>andong> the

former USSR: A reassessment. Science, 293, 474–



D. Easterling, T. Peterson, ong>andong> T. Karl, 2001: A

closer look at United States ong>andong> global surface temperature

change. J. Geophys. Res., 106, 23947–


Huntington, T. G., 2006: Evidence for intensification ong>ofong>

the global water cycle: Review ong>andong> synthesis. Journal

ong>ofong> Hydrometeorology, 319, 83–95.

IPCC, 2007: ong>Climateong> ong>Changeong> 2007 : The Physical Science

Basis. Contribution ong>ofong> Working Group I to the

Fourth Assessment Report ong>ofong> the Intergovernmental

Panel on ong>Climateong> ong>Changeong>. Solomon et al., Eds.,

Cambridge Univ. Press, New York, 996pp.

Jones, P. D., ong>andong> A. Moberg, 2003: Hemispheric ong>andong>

large-scale surface air temperature variations: An extensive

revision ong>andong> update to 2001. J. ong>Climateong>, 16,


Le Barbe, L., T. Lebel, ong>andong> D. Tapsoba, 2002: Rainfall

variability in West Africa during the years 1950–

1990. J. ong>Climateong>, 15, 187–202.

Lugina, K. M., P. Y. Groisman, K. Y. Vinnikov, V. V.

Koknaeva, ong>andong> N. A. Speranskaya, 2005: Monthly

surface air temperature time series area-averaged

over the 30-degree latitudinal belts ong>ofong> the globe,

1881–2004. Trends: A Compendium ong>ofong> Data on

Global ong>Changeong>. Carbon Dioxide Information Analysis

Center, Oak Ridge National Laboratory, US

Department ong>ofong> Energy, Oak Ridge, TN. [Available at:


Mitchell, T. D., ong>andong> P. D. Jones, 2005: An improved

method ong>ofong> constructing a database ong>ofong> monthly climate

observations ong>andong> associated high-resolution

grids. International Journal ong>ofong> Climatology, 25(6),


Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar ong>andong>


R. R. Nemani, 1997: Increased plant growth in the

northern high latitudes from 1981 to 1991. Nature,

386, 698–702, doi: 10.1038/396698a0.

Nozawa, T., T. Nagashima, H. Shiogama, ong>andong> S. Crooks,

2005: Detecting natural influence on surface air temperature

in the early twentieth century. Geophys.Res.

Lett., 32, L20719, doi: 10.1029/2005GL023540.

Ohmura, A., ong>andong> M. Wild, 2002: Is the hydrological cycle

accelerating? Science, 298, 1345–1346.

Oleson, K. W., ong>andong> Coauthors, 2004: Technical Description

ong>ofong> the Community Long>andong> Model (CLM3).


Boulder, Colorado, 186pp.

Oleson, K. W., ong>andong> Coauthors, 2008: Improvements to

the Community Long>andong> Model ong>andong> their impact on the

hydrological cycle. J. Geophy. Res., 113, G01021,

doi: 10.1029/2007jg000563.

Peterson, T. C., ong>andong> R. S. Vose, 1997: An overview ong>ofong>

the Global Historical Climatoloty Network temperature

database. Bulletin ong>ofong> the American Meteorological

Society, 78, 2873–2848.

Peterson, T. C., V. S. Golubev, ong>andong> P. Y. Groisman, 1995:

Evaporation losing its strengthe. Nature, 377, 687–


Qian, T., A. Dai, ong>andong> K. E. Trenberth, 2007: Hydroclimatic

trends in Mississippi River basin from 1948-

2004. J. ong>Climateong>, 20, 4599–4614.

Robinson, P. J., 2000: Temporal trends in United States

dew point temperatures. International Journal ong>ofong>

Climatology, 20, 985–1002.

Roderick, M. L., ong>andong> G. D. Farquhar, 2004: ong>Changeong>s

in Australian Pan Evaporation from 1970 to 2002.

International Journal ong>ofong> Climatology, 24(9), 1077–


Rudolf, B., H. Hauschild, W. Rueth, ong>andong> U. Schneider,

1994: Terrestrial precipitation analysis: Operational

method ong>andong> required density ong>ofong> point measurements.

Global Precipitations ong>andong> ong>Climateong> ong>Changeong>,

Bubois ong>andong> Désalmong>andong>, Eds., NATO ASI Series I,

26, Springer Verlag, Berlin, 173–186.

Running, S. W., D. D. Baldocchi, D. P. Turner, S. T.

Gower, P. S. Bakwin, ong>andong> K. A. Hibbard, 1999:

A global terrestrial monitoring network integrating

tower fluxes, flask sampling, ecosystem modeling ong>andong>

EOS satellite data. Remote Sens. Environ., 70, 108–


Sheffield, J., G. Goteti, ong>andong> E. F. Wood, 2006: Development

ong>ofong> a 50-year high-resolution global dataset ong>ofong>

meteorological forcings for long>andong> surface modeling. J.

ong>Climateong>, 19, 3088–3111.

Smith, T. M., ong>andong> R. W. Reynolds, 2005: Improved extended

reconstruction ong>ofong> SST (1854-1997). J. ong>Climateong>,

18, 2021–2036.

Stocker, T. F., ong>andong> C. C. Raible, 2005: Water cycle shift

gear. Nature, 434, 830–833.

Stöckli, R., ong>andong> Coauthors, 2008: Use ong>ofong> FLUXNET

in the Community Long>andong> Model development. Journal

ong>ofong> Geophysical Research, 113, G01025, doi:



T. Nakajima, 2005: Simulation ong>ofong> climate response

to aerosol direct ong>andong> indirect effects with aerosol

transport-radiation model. J. Geophys. Res., 110,

D02202, doi: 10.1029/2004JD005029.

Walter, M. D., D. S. Wilks, J. Y. Parlange, ong>andong> R. L.

Schneider, 2004: Increasing evapotranspiration from

the conterminous United States. Journal ong>ofong> Hydrometeorology,

5, 405–408.

Wang, J. X. L., ong>andong> D. J. Gaffen, 2001: Trends in extremes

ong>ofong> surface humidity, temperatures ong>andong> summertime

heat stress in China. Adv. Atmos. Sci., 18,


Wild, M. A., A. Ohmura, H. Gilgen, ong>andong> D. Rosenfeld,

2004: On the consistency ong>ofong> trends in radiation ong>andong>

temperature records ong>andong> implications for the global

hydrological cycle. Geophys. Res. Lett., 31, L11201,

doi: 10.1029/2003GL019188.

Xiao, J., ong>andong> Coauthors, 2008: Estimation ong>ofong> net ecosystem

carbon exchange for the conterminous United

States by combining MODIS ong>andong> AmeriFlux data.

Agricultural ong>andong> Forest Meteorology, 148, 1827–1847.

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