Energy budget change in the tropics according to the SRES A1B scenario in the IPCC AR4 models

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, , doi: /jgrd.50240, 2013 Energy budget change in the tropics according to the SRES A1B scenario in the IPCC AR4 models Kwang-Yul Kim, 1 Hanna Na, 2 and Hun-Gyu Lee 1 Received 24 June 2012; revised 29 January 2013; accepted 31 January 2013; published 25 March [1] The IPCC AR4 model data sets were investigated in order to address how the energy balance will change in the tropics (30 S 30 N) under the SRES A1B scenario. A climate change signal with a well-defined trend was extracted using cyclostationary empirical orthogonal function analysis; this signal depicts near-linear warming in the tropics. This warming signal, together with the seasonal cycle, explains most of the variance ( %) in the AR4 model datasets. In addition to the warming of the atmospheric column and the surface, the cloud fraction decreased over most of the tropics, except over the equatorial Pacific. Specific humidity generally increased over the entire troposphere. The decreased cloud fraction and the increased specific humidity resulted in a net increase in shortwave radiation (by ~3.58 (1.92) W m 2 ) in the atmospheric column. Simultaneously, the increased atmospheric and surface temperatures (resulting from positive water vapor feedback) caused enhanced longwave radiation exchange between the surface and the atmospheric column; net downward longwave radiation increased by ~19.22 (3.85) W m 2, and net upward longwave radiation increased by ~14.50 (3.04) Wm 2 over 100 years. The second largest change was found in the heat flux leaving the surface, which amounted to ~4.55 (1.72) W m 2. As a result of the radiation budget change associated with warming and meridional energy transport by the atmosphere and the ocean, net energy gains were found for both the tropical atmosphere (~5.82 (3.23) W m 2 ) and the tropical surface (~0.48 (0.30) W m 2 ). Citation: Kim, K.-Y., H. Na, and H.-G. Lee (2013), Energy budget change in the tropics according to the SRES A1B scenario in the IPCC AR4 models, J. Geophys. Res. Atmos., 118, , doi: /jgrd Introduction [2] The 4th Assessment Report (AR4) published by the Intergovernmental Panel on Climate Change (IPCC) stated that the global average surface temperature has increased since the middle of the nineteenth century. Under the A1B scenario in the IPCC AR4, the average surface temperature and the sea level will further increase by C and m, respectively, in the next 100 years [Solomon et al., 2007]. The potential climate changes in the 21st century have been extensively studied using the AR4 model results, which constitute the most frequently used and easily available datasets for studying future climate changes. For example, Held and Soden [2006] investigated hydrological cycle changes due to global warming based on the IPCC AR4 model results. The precipitation extreme changes [Sugiyama et al., 2010] and the weakening of tropical 1 School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea. 2 Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island, USA. Corresponding author: K-Y. Kim, School of Earth and Environmental Sciences, Seoul National University, 1 Gwanangno, Gwanak-gu, Seoul , Republic of Korea. (Kwang56@snu.ac.kr) American Geophysical Union. All Rights Reserved X/13/ /jgrd circulation [Vecchi and Soden, 2007] under future climate warming have also been investigated based on the IPCC AR4 model results. Additionally, Wild [2008] compared the observed surface radiation budget and the predicted budget in the IPCC AR4/CMIP3 (Coupled Model Intercomparison Project) models; they showed qualitatively similar but quantitatively smaller biases compared to the earlier models. [3] Understanding energy budget change is essential for future climate change studies. Solar (shortwave) radiation is a critical energy source for the Earth s climate system and a driving force for global atmospheric and oceanic circulations [Wielicki et al., 2002]. In addition, the Earth s energy budget is a predominant factor in determining not only climatic variability and feedback [Wielicki et al., 2002] but also ocean temperatures and sea level [Domingues et al., 2008]. Absorbed terrestrial (longwave) radiation at the surface resulting from the greenhouse effect plays an important role in the Earth s climate system [Ramanathan, 1987; Wielicki et al., 2002]. Based on the AR4 model results, Trenberth and Fasullo [2009] argued that the increased solar radiation absorption as a result of the positive cloud feedback with reduced cloud amount amplifies global warming caused by anthropogenic forcing. [4] Although several studies have focused on Arctic energy budget changes using the AR4 model datasets [Sorteberg et al., 2007; Gorodetskaya et al., 2008], energy budget changes in the tropics have not been as heavily investigated. 2521

2 The tropics (30 S 30 N), however, significantly influence global energy balance. For instance, insolation in the tropics represents nearly two thirds of the total energy received by the Earth. Furthermore, equator-to-pole radiative heating gradients induce the meridional heat transport through oceanic and atmospheric circulations [Ramanathan, 1987]. Therefore, energy budget changes in the tropics could alter the climate not only in the tropical region but also at higher latitudes. Global energy budget may not clearly depict the tropical energy change associated with an increasing concentration of greenhouse gases, since energy budget change in the tropics may be significantly different from that in the polar region or the mid-latitudes. For these reasons, it is important to understand how the energy budget will change in the tropics over time as a function of the continuous emission of greenhouse gases. [5] Previous studies have exclusively focused on tropical energy budget changes at the top of the atmosphere (TOA) or at the surface [Sorteberg et al., 2007; Andronova et al., 2009; Wild, 2008]. It is necessary, however, to understand how the energy budget changes simultaneously at the surface, in the atmospheric column, and at the top of the atmosphere. Energy exchange between the surface and the atmospheric column and between the atmospheric column and the top of the atmosphere is not only an important component of the Earth s climate system but also a crucial factor for controlling the vertical distribution of energy, moisture and momentum, and the strength and pattern of global and regional circulation. Therefore, in the present study, we examined detailed energy budget changes in the tropics throughout the atmospheric column, including the surface and the top of the atmosphere. In order to determine the energy budget in the tropics, meridional energy transport was also investigated in the atmosphere and in the ocean. [6] To describe future climate changes, most studies have examined differences in the mean tropical energy budget of the first 20 (or 10) years and that of the last 20 (or 10) years [Held and Soden, 2006; Vecchi and Soden, 2007; Gorodetskaya et al., 2008]. Other studies have used the difference between control runs and the emission scenarios [Sorteberg et al., 2007; Vecchi and Soden, 2007; Ihara et al., 2009] to capture changes in the 21st century. However, the natural variability inherent in these simulations can obscure true climate change signals even with a 20 year averaging; therefore, it is difficult to estimate how much of the 100 year difference is due to natural variability and how much is truly due to the increased concentration in greenhouse gases. While such a practice is common, there is always a tradeoff between a sufficient reduction of natural variability and an accurate estimation of the magnitude of climate change. An optimal length of averaging is not obvious at the outset; further, the magnitude of natural variability varies in individual variables. In addition, 100 year difference shows only the pattern of climate change not the evolution history; the latter may often be an important aspect of climate changes. [7] In the present study, climate change signals, together with the evolution history, were explicitly extracted using the cyclostationary empirical orthogonal function (hereafter CSEOF) technique [Kim et al., 1996; Kim and North, 1997] and were investigated independently of other natural fluctuations. An attempt to extract the climate change signal in its entirety, while retaining physical consistency among different physical variables, is both important and novel. [8] The data used in this study are described in section 2; fifteen AR4 model data sets were used to investigate tropical energy budget changes according to a future projection of the concentration of greenhouse gases. The CSEOF technique and the regression method in CSEOF space are described in section 3. In section 4, the results of the analyses of energy budget change in the tropics are presented. A summary and discussion of these findings follows in section Data [9] Fifteen IPCC AR4 model datasets (as summarized in Table 1) were used in the present study. The model datasets employed here simulate the climate of the earth according to the A1B scenario (median forcing) in the Special Reports on Emissions Scenarios (SRES) published by the IPCC [Nakicenovic et al., 2000]. According to the A1B scenario, the concentration of carbon dioxide will increase continuously from the year 2000 until it stabilizes at 700 ppm in 2100 [Meehl et al., 2007] (Figure 1). Thus, these datasets are suitable for understanding the impact of anthropogenic influence on the energy balance of the Earth in the tropics. [10] The present study utilized the monthly averages from the period between 2000 and 2100, and these values are Table 1. List of the AR4 Models Used in the Present Study Institute Model name Resolution Bjerknes Centre for Climate Research BCCR-BCM Canadian Centre for Climate Modeling and Analysis CCCMA-CGCM3.1-T Canadian Centre for Climate Modeling and Analysis CCCMA-CGCM3.1-T Centre National de Recherches Meteorologiques CNRM-CM NOAA Geophysical Fluid Dynamics Lavoratory GFDL-CM NOAA Geophysical Fluid Dynamics Lavoratory GFDL-CM LASG, Institute of Atmospheric Physics IAP-FGOALS-g Institute for Numerical Mathematics INM-CM CCSR/NIES/FRCGC MIROC3.2-hires CCSR/NIES/FRCGC MIROC3.2-medres Max Planck Institute for Meteorology MPI-ECHAM5/MPI OM Meteorological Research Institute MRI-CGCM National Center for Atmospheric Research NCAR-CCSM Hadley Centre for Climate Prediction, Met Office UKMO-HadCM Hadley Centre for Climate Prediction, Met Office UKMO-HadGEM

3 concentration (ppmv) Concentration of CO time (year) Concentration of CH 4 concentration (ppbv) Concentration of N 2 O time (year) Concentration of CFC-11* concentration (ppbv) concentration (pptv) time (year) time (year) Figure 1. Concentration of greenhouse gases for the IPCC A1B scenario (data adopted from cnrm.meteo.fr/ensembles/public/results/results.html). available at the standard vertical levels. The selected model data sets contain all of the variables analyzed in the present study: geopotential height, air temperature, wind, specific humidity, total cloud fraction, latent and sensible heat fluxes, and downward and upward longwave and shortwave radiations. [11] Energy in the atmosphere was calculated from the model datasets. Expressions for the intrinsic forms of energy per unit mass are given by IE ¼ c v T; PE ¼ gz; LE ¼ Lq; and KE ¼ 1 2 u2 þ v 2 þ w 2 ; (1) where c v is specific heat at constant volume (assumed a constant: 717 J kg 1 K 1 ), T is the temperature of air, g is the gravitational acceleration, z is elevation, L (= Jkg -1 )is the latent heat of evaporation, q is specific humidity of air, and (u,v,w) are the zonal, meridional, and vertical components of velocity, respectively. Each term in (1) represents internal energy, potential energy, latent energy, and kinetic energy, respectively. Meridional energy transport (W m 1 ) in the atmosphere was derived via HTðÞ¼ z Z z r c p T þ gz þ Lq þ 1 2 c2 vdz; (2) 0 where r is the density of air, c p is specific heat at constant pressure, and c is wind speed. All the variables in the integration are a function of z. For simplification, however, we assumed a constant specific heat (1014 J kg 1 K 1 ) and the standard density profile rðþ¼r z 0 expð z=hþ; (3) where r 0 = kg m 2 and the scale height H = 8 km. This profile is a reasonable assumption in the tropics. Meridional heat transport in the ocean was also used; however, this variable is available in only 8 of the 15 models. [12] Several of the datasets have different resolutions, and no attempt has been made to interpolate the data sets onto a common grid. In the present study, the tropical region (30 S 30 N) was chosen, where the radiation energy budget is of significant impact for global circulations and the energetics of the entire earth. The IPCC AR4 data sets used in this study are available at the PCMDI software portal ( [13] We also used ECMWF (European Centre for Medium- Range Weather Forecasts) ERA-40 reanalysis data [Uppala et al., 2005] and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data [Kalnay et al., 1996] to calculate the seasonal cycle of key variables, energy, and energy transport; they were compared with those derived from the 15 AR4 models employed in the present study. 3. Method of Analysis 3.1. CSEOF Analysis [14] To separate the climate change mode from the data sets, CSEOF analysis was used in the present study [Kim et al., 1996; Kim and North, 1997]. InCSEOFanalysis, space-time data are decomposed into Pr; ð t Þ ¼ X B n nðr; tþt n ðþ; t (4) where B n (r,t) are CSEOF loading vectors (LV), T n (t) are corresponding principle component (PC) time series, and r and t denote space and time, respectively. Unlike EOF loading vectors, each CSEOF LV consists of several spatial patterns describing the evolution of a physical variable. The corresponding PC time series represents the amplitude of that 2523

4 evolution. Furthermore, CSEOF LVs are periodic with the nested period, d. Thatis, B n ðr; tþ ¼ B n ðr; t þ dþ; (5) [15] Because solar radiation, the primary driving force for the climate system, has a 1 year periodicity, the nested period is assumed to be one year (12 months) in the present study. As a result, each LV has 12 spatial patterns, with one pattern for each month Regression Analysis [16] Regression analysis is conducted in the CSEOF space in order to ensure physically consistent evolution of all of the variables. This process consists of two steps. In the first step, multiple regression is conducted between a target PC time series and a predictor PC time series: T n ðþ¼ t X M a ðþ n m¼1 m P mðþþe t ðnþ ðþ; t (6) where T n (t) isthenth PC time series of a target variable, P m (t) is the PC time series of a predictor variable, a ðþ m n is the regression coefficients, and e (n) (t) is the regression error time series. In the second step, physically consistent patterns of the predictor variable are obtained via D n ðr; tþ ¼ X M a ðnþ m¼1 m C mðr; tþ; (7) where C m (r,t) is the CSEOF LVs of the predictor variable. Then the evolution of the target variable, B n (r,t), and that of the predictor variable, D n (r,t), are said to be physically consistent. It should be emphasized that the two evolutions are not identical but share the same amplitude time series. Details of this procedure can be found in previous research [Seo and Kim, 2003; Kim and Roh, 2010; Kim et al., 2010]. [17] In the present study, 300 hpa geopotential height was selected as the target variable, as the linear-trending climate change signal is unambiguously separated from this variable in all the model datasets. Afterwards, all other variables (predictor variables) were regressed onto the target variable, yielding physically consistent evolution of all variables [see Seo and Kim, 2003; Kim and Roh, 2010; Kim et al., 2010; Kim et al., 2012a; Kim et al., 2012b]. As a result of the regression analysis in CSEOF space, the entire set of data is written as Dataðr; tþ ¼ X f T n nðr; tþ; u n ðr; tþ; v n ðr; tþ; q n ðr; tþ;... gt n ðþ; t (8) where the terms in curly braces represent the evolution of the variables examined in the present study. 4. Results [18] Climate change signals pertaining to the SRES A1B scenario have been extracted from the 15 IPCC AR4 model datasets in Table 1. These models contain all of the relevant variables for computing the energy budget in the atmosphere and at the surface. CSEOF analysis identified the seasonal cycle and the climate change signal as the two dominant modes in each model dataset; taken together, the two modes explain the majority of the variance in the datasets (Table 2). Table 3 shows the R 2 values of regression for all 15 AR4 models. The R 2 values are greater than except for downward shortwave radiation at the top of the atmosphere; downward shortwave radiation at the top of the atmosphere apparently has little connection with global warming Changes in the Tropical Thermodynamic Variables [19] The spatial patterns and magnitudes of the seasonal cycle for key variables are similar to those extracted from the ECMWF ERA-40 reanalysis data or the NCEP/NCAR reanalysis data (figure not shown). The seasonal cycle under the A1B scenario is also similar to the cycle that was extracted from the pre-industrial control run, which is based on pre-industrial conditions in the late nineteenth century. The averaged PC time series shows no significant trend, although individual PC time series exhibit weak trends in both directions. It appears that the seasonal cycle is not seriously altered by the increased concentration of greenhouse gases predicted by the A1B scenario. Table 2. Percentage of Total Variance in Unit of ( C) 2 Explained by the First Two CSEOF Modes a Total % of Total Variance Models Variance Mode 1 Mode 2 Modes BCCR-BCM E CCCMA3.1-T E CCCMA3.1-T E CNRM-CM E GFDL-CM E GFDL-CM E IAP-FGOALS-g E INM-CM E MIROC3.2-hires 5.356E MIROC3.2-medres 3.911E MPI-ECHAM E MRI-CGCM E NCAR-CCSM E UKMO-HadCM E UKMO-HadGEM E a The seasonal cycle (in italics) and the climate change signal (in boldface). 2524

5 Table 3. The R 2 Values of Regression Averaged for the 15 AR4 Models a Variable Variable Description Mean STD TA (20) Atmospheric temperature ZG (20) Geopotential height UVA (20) Zonal and meridional wind WAP (20) Vertical pressure velocity HUS (20) Specific humidity HTO (20) Heat transport in the ocean CLT (50) Total cloud fraction PR (50) Precipitation rate HFLS (50) Latent heat flux HFSS (50) Sensible heat flux RSDT (20) Downward shortwave radiation at top of atmosphere RSUT (50) Upward shortwave radiation at top of atmosphere RSDS (50) Downward shortwave radiation at surface RSUS (50) Upward shortwave radiation at surface RLUT (50) Upward longwave radiation at top of atmosphere RLDS (50) Downward longwave radiation at surface RLUS (50) Upward longwave radiation at surface a The number in parenthesis next to the variable name indicates the number of predictor PC time series used for regression. Variable RSDT has less than 20 PC time series. All available PC time series were used for regression. [20] Figure 2 shows the CSEOF loading vector and the corresponding PC time series of the climate change mode. Annual average patterns are presented as much as possible in this study, as there is no significant monthly evolution. Loading vectors show positive air temperature anomalies at each level. At the surface level (1000 hpa), the magnitude of warming differs appreciably from one location to another (Figure 2e). The magnitude of warming relative to the climatology becomes more uniform at upper levels, with the largest overall magnitude of warming being found in the upper troposphere (Figure 4). Greater warming in the upper troposphere relative to the surface indicates that current atmospheric warming trends [Allen and Sherwood, 2008] will likely continue. Because the variations of geopotential height are related to the structure of air temperature [Peixoto and Oort, 1992], geopotential height in the troposphere (especially around tropopause) will also increase. This finding suggests that the height of the tropopause will rise and the tropical belt will widen in the 21st century [Seidel et al., 2008]. Also, there is an increase of vertical wind shear, resulting from warming in the upper troposphere relative to the lower troposphere [Allen and Sherwood, 2008]. The normalized PC time series from each model exhibits an increasing, nearly linear trend, which is similar among the models. Judging from the PC time series, this mode represents warming in the tropical troposphere; no other mode exhibits such a near-linear trend. Hereafter, this mode will be referred to as the climate change signal Changes in the Tropical Convection [21] Figure 3 shows the variables associated with convection. With tropospheric warming, cloud fraction generally decreases over the tropics, except over the equatorial Pacific. The increased cloud fraction over the equatorial Pacific is due to the strengthening of convection, as reflected in the increased upward velocity at 500 hpa and in precipitation. Specific humidity at 500 hpa has increased over the entire tropics, particularly over the equatorial Pacific, due to the warming of the atmospheric column. In the presence of convection and subsequent upward motion, cloudiness and precipitation increase. Without convection, conversely, cloudiness decreases because relative humidity decreases due to the warming of the atmospheric column. [22] Figure 4 shows the vertical structure of the climate change signal averaged between 10 S and 10 N. Air temperature increases in the troposphere; a maximum increase occurs at approximately 200 hpa. With the warming of the atmospheric column, geopotential height also increases with a maximum increase at approximately 70 hpa. Specific humidity also increases in the lower 300 hpa of the troposphere because evapotranspiration over the ocean and the land increases as surface temperature increases in the tropics. Upward motion and corresponding circulation, by contrast, are weakened in the tropics, particularly over the equatorial Indian Ocean [Vecchi and Soden, 2007], although not in the equatorial eastern Pacific. The anomalous circulation pattern implies that the Walker circulation diminishes, reducing zonal sea surface temperature gradient in the tropical Pacific. The weakened upward motion is another reason why cloud fraction, on average, decreases in the tropics with the exception of the equatorial Pacific. The increased cloud fraction over the equatorial Pacific and the decreased cloud fraction over the other region undoubtedly have a significant impact on the radiation budget change in the tropics Changes in the Tropical Energy Budget [23] Figure 5 shows the zonal mean pattern of the 100 year change in absorbed solar radiation, outgoing longwave radiation, net downward radiation, and cloud fraction as a function of the time of year. Absorbed solar radiation increased throughout the year particularly in summer (JJA in NH and OND in SH). Outgoing longwave radiation also increased over the tropics except near the equator, where outgoing radiation decreased from July to January. The pattern of outgoing longwave radiation change is fairly similar to the pattern of cloud fraction reduction, implying that outgoing longwave radiation increased due to the cloud fraction reduction in the tropics (Figure 5d). The magnitude of outgoing longwave radiation change is in general slightly smaller than that of the absorbed solar radiation, resulting in 2525

6 KIM ET AL.: TROPICAL ENERGY BUDGET IN AR4 A1B MODELS Figure 2. The second CSEOF mode of MIROC3.2-hires data (upper) and the corresponding PC time series (lower). This mode represents the climate change (warming) signal. Other models depict similar climate change. In the lower panel, the bold black line denotes the average of all (normalized) PC time series from different model datasets (thin colored lines). Figure 3. Annual average pattern of (a) cloud fraction, (b) 500 hpa vertical ( Ω) velocity, (c) precipitation, and (d) 500 hpa specific humidity in association with the climate change mode. The amplitude (PC) time series is shown in Figure 2. increased net downward radiation except during winter. Increase in net downward radiation is prominent near the equator. Figure 5 is reasonably similar to Figure 12 in Trenberth and Fasullo [2010]. Spatial pattern of absorbed solar radiation (figure not shown) and that of cloud fraction (Figure 3a) are also similar to, respectively, Figures 9 and

7 Figure 4. Annual pattern of vertical structure averaged over 10 S 10 N of (a) air temperature, (b) geopotential height, (c) specific humidity, and (d) vertical ( Ω) velocity in association with the climate change mode. The amplitude (PC) time series is shown in Figure 2. Figure 5. Zonal mean pattern of 100 year change from the climate change mode as a function of time of year: (a) absorbed solar radiation at top and (b) outgoing longwave radiation at top, (c) net downward radiation at top, and (d) cloud fraction. in Trenberth and Fasullo [2010]. Energy budget, including the vertical and horizontal energy fluxes, will be discussed in detail below. [24] Figure 6 shows the radiation change associated with the climate change signal. At the top of the atmosphere, net absorbed radiation increases over the tropical Pacific because of increased cloudiness; the reduction of outgoing longwave radiation over that region is greater than the reflected shortwave radiation (Figure 6a). Otherwise, net absorbed radiation is generally positive and weak, except for small negative values at scattered locations. Change in total downward (shortwave) radiation at the top of the atmosphere is insignificant, indicating that solar radiation reaching the earth from the sun remains nearly constant (Figure 7). At the surface, both total downward radiation and total upward radiation increase substantially. The increase of total downward radiation (Figure 6b) is generally greater than that of total upward radiation (Figure 6c), indicating that there is a surplus of radiation at the surface. On the other hand, latent heat flux significantly increases over most of the ocean s surface, except near the western equatorial Pacific. [25] Increased greenhouse gases induce tropospheric warming, which, in turn, results in increased specific humidity in the troposphere. Although the specific humidity increases, relative humidity decreases because of large increase in air temperature [Schneider et al., 1999]. Accordingly, decreased total cloud fraction results from decreases in relative humidity [Teixeira, 2001] and upward motion. As a result, increased specific humidity and decreased total cloud fraction in the troposphere affect the atmospheric warming. Water vapor is chief among the greenhouse gases in the atmosphere [Sun and Lindzen, 1993] and is an effective absorber of shortwave and longwave radiation. Thus, increased specific humidity contributes to enhanced energy in the atmospheric column by trapping downward shortwave radiation and upward longwave radiation [Peixoto and Oort, 1992]. 2527

8 KIM ET AL.: TROPICAL ENERGY BUDGET IN AR4 A1B MODELS Figure 6. Annual average pattern of (a) net absorbed radiation at top, (b) total downward radiation at surface, (c) total upward radiation at surface, and (d) total upward heat flux in association with the climate change mode. The amplitude (PC) time series is shown in Figure 2. Figure 7. Annual average energy budget in the tropics (30 S 30 N) in 15 AR4 models (above) and the corresponding PC time series (below) in association with the climate change signal. The black bold-faced numbers represent mean values based on 15 model datasets, and the numbers in red and blue boxes are changes and the 1s-level of natural variability in parenthesis over 100 years according to climate change signal under the A1B scenario. The gray boxes contain net changes in radiation and energy transport in the atmospheric column and the surface. [26] Figure 7 summarizes the energy budget changes in the tropics. All of the variables in Figure 7 were available in the currently analyzed models. In addition, meridional energy transport in the atmospheric column was calculated according to (2) and was analyzed together with ocean heat flux. Numbers in bold in Figure 7 represent the mean values 2528

9 based on the 15 AR4 models. As can be seen, energy balance is achieved except for a small net surplus of energy in the atmospheric column and at the surface; this small net surplus of energy indicates increasing temperature of the atmosphere and the surface. Numbers in the boxes denote changes over 100 years and the 1s values of natural variability according to the 15 AR4 models. The 100 year change for each model was derived from a linear fit to the PC time series of the climate change mode as in Figure 2g. The number in parenthesis denotes one standard deviation of natural variability averaged over the 15 AR4 models; the 1s values of natural variability is calculated after removing the monthly climatology and the climate change mode for each model. Energy budgets at the top of the atmosphere, at the surface, and in the atmospheric column for each model are summarized in Tables 4, 5, and 7, respectively. Furthermore, ocean and atmospheric energy transports, together with the net energy changes, are summarized in Tables 6 and 8, respectively Energy Budget Change in the Vertical Direction [27] At the top of the atmosphere, longwave radiation leaving the atmospheric column increases by ~1.84 (1.63) W m 2, whereas reflected shortwave radiation decreases by ~3.05 (2.04) W m 2, resulting in a net radiation increase of ~1.21 (0.75) W m 2 in the tropics (Table 4). The largest change in the energy budget occurs in the longwave radiation exchange between the atmospheric column and the surface. Table 6. Changes in the Amounts of Heat Loss (W m 2 ) Through Ocean Heat transport and Net Energy Gain in the Tropical Ocean (30 S 30 N) Models 100 Year Heat Transport Net Energy Gain BCCR-BCM CNRM-CM FGOALS-g INM-CM MIROC3.2-hires MIROC3.2-medres MRI-CGCM UKMO-HadCM Average Standard deviation Table 4. Energy Budget Change (W m 2 ) Over 100 Years at the Top of the Atmosphere in Association With the Climate Change Signal for Each Model Models (a) Shortwave(Incoming) (b) Shortwave(Outgoing) (c) Longwave(Outgoing) Net= (a) (b) (c) BCCR-BCM CCCMA3.1-T CCCMA3.1-T CNRM-CM GFDL-CM GFDL-CM IAP-FGOALS-g INM-CM MIROC3.2-hires MIROC3.2-medres MPI-ECHAM MRI-CGCM NCAR-CCSM UKMO-HadCM UKMO-HadGEM Average Standard deviation Table 5. Energy Budget Change (W m 2 ) Over 100 Years at the Surface in Association With the Climate Change Signal for Each Model Models (a) Shortwave (Absorbed) (b) Longwave (Absorbed) (c) Longwave (Emitted) (d) Heat Flux Total = (a) + (b) (c) (d) BCCR-BCM CCCMA3.1-T CCCMA3.1-T CNRM-CM GFDL-CM GFDL-CM IAP-FGOALS-g INM-CM MIROC3.2-hires MIROC3.2-medres MPI-ECHAM MRI-CGCM NCAR-CCSM UKMO-HadCM UKMO-HadGEM Average Standard deviation

10 At the surface, we find a ~19.22 (3.85) W m 2 increase in downward longwave radiation and a ~14.50 (3.04) W m 2 increase in upward longwave radiation on average (Table 5). As a result, there is a net increase of downward longwave radiation of ~4.72 (1.20) W m 2 at the surface. Warming of the surface, as well as warming of the atmospheric column, explains the large increase in longwave radiation budget at the surface. The increased specific humidity (Figure 4c) and the increased longwave radiation from the surface may be responsible for the warming of the atmospheric column; separation of these two effects is difficult based on data analysis alone and is not attempted in the present study. In addition, there is a~4.55(1.72) W m 2 increase in heat flux, which mainly results from the emitted latent heat flux at the sea surface. The next largest radiation budget change is in the shortwave radiation that is absorbed in the atmospheric column (an increase of ~3.58 (1.92) W m 2 ; Table 7). This increase is likely due to the increased concentration of water vapor. Decreased total cloud fraction causes increased solar radiation in the atmospheric column, which contributes to the increased absorption of solar radiation in the atmospheric column. [28] The energy budget analysis in the vertical direction shows that there is a net radiation surplus of ~1.21 (0.75) at the top of the atmosphere (Table 4), net deficit of ~0.39 (0.50) W m 2 at the surface (Table 5), and a net surplus of ~1.63 (0.65) W m 2 in the atmospheric column (Table 7). Although the net radiation change is small from the surface to the top of the atmosphere, major climate change terms addressed above are much bigger than the spread (1s value) of the 15 AR4 models. They are also well beyond the respective natural variability in magnitude as shown in Figure 7 (values in parenthesis) Energy Change in the Atmospheric Column [29] Energy in the atmosphere was calculated from the pre-industrial control runs and the A1B runs as shown in Figure 8. Internal energy and potential energy dominate and explain more than 97% of the total energy in the atmosphere. There is only a slight increase in the amount of total energy in the A1B runs, although there is an appreciable fractional increase in latent energy and kinetic energy. [30] Energy change in 100 years in the A1B runs is also estimated by subtracting the first 20 year averages from the last 20 year averages since energy is not a directly observable quantity. Internal energy and potential energy increased by about 1%, while latent energy and kinetic energy increased by about 13% and 6%, respectively. The increased latent Table 7. Energy Budget Change (W m 2 ) Over 100 Years in the Atmospheric Column in Association With the Climate Change Signal for Each Model Models (a) Shortwave (Absorbed) (b) Longwave (Absorbed) (c) Heat Flux (d) Longwave (Emitted) Total = (a) + (b) + (c) (d) BCCR-BCM CCCMA3.1-T CCCMA3.1-T CNRM-CM GFDL-CM GFDL-CM IAP-FGOALS-g INM-CM MIROC3.2-hires MIROC3.2-medres MPI-ECHAM MRI-CGCM NCAR-CCSM UKMO-HadCM UKMO-HadGEM Average Standard deviation Figure 8. Averaged energy budget in the pre-industrial control runs (left) and that in the A1B runs (right). Each box represents internal energy (IE), potential energy (PE), latent energy (LE), and kinetic energy (KE), respectively, as defined in Peixoto and Oort [1992]. In the A1B runs, the third row in red represents the 100 year difference (last 20 year average minus first 20 year average). Values in parenthesis denote one-standard deviations derived from the 15 models. 2530

11 energy may not be readily realized in the atmosphere because of the increased saturation specific humidity (Figure 4c); moister atmospheric column, however, is potentially more explosive. Also, the increased kinetic energy implies that (mainly horizontal) movement of air in the atmospheric column became stronger Energy Transport Change in the Atmosphere and the Ocean [31] Figure 9 shows the zonally averaged vertical structure of internal energy and potential energy transport for the AR4 models in comparison with those of the NCEP/NCAR and ECMWF reanalysis datasets. As can be seen, AR4 models result in vertical structures similar to those of the reanalysis datasets. Changes in internal energy and potential energy transport in 100 years are shown in Figure 10 and Table 8. As can be seen in Table 8, changes in latent and kinetic energy transports (total minus the sum of internal and potential energy transports) are very small compared to those of internal and potential energy transports; therefore, the former changes will not be addressed here. A significant change in internal energy transport is seen near the surface particularly near the equator. Change in potential energy transport is clearly seen near the tropopause. It appears that the elevation of the maximum potential energy transport increased in the A1B runs, increasing potential energy transport above the tropopause and decreasing it below the tropopause (Figure 10d). This reflects general warming of the troposphere in the AR4 models and the resulting increase in tropopause height [Santer et al., 2004]. Figure 9. Transport of internal energy (left column) and potential energy (right column) in AR4 models (top panels), NCEP/NCAR reanalysis data (middle panel), and ECMWF ERA-40 reanalysis data (bottom panels). Figure 10. Vertical structure of transport of internal energy (left column) and potential energy (right column) in AR4 models and their 100 year changes (lower panels) due to the climate change signal. 2531

12 Table 8. Changes in the Amount of Heat Loss (W m 2 ) Through Atmospheric Transport of Internal Energy (Second Column), Potential Energy (Third Column), and Total Moist Static Energy (Fourth Column) and Net Energy Gain in the Tropical Atmosphere (30 S 30 N) Models Internal Energy Transport Potential Energy Transport 100 yr Total Energy Transport Net Energy Gain BCCR-BCM CCCMA3.1-T CCCMA3.1-T CNRM-CM GFDL-CM GFDL-CM IAP-FGOALS-g INM-CM MIROC3.2-hires MIROC3.2-medres MPI-ECHAM MRI-CGCM NCAR-CCSM UKMO-HadCM UKMO-HadGEM Average Standard deviation [32] Reduced oceanic heat transport of ~0.88 (0.55) W m 2 out of the tropics compensates for energy loss at the surface, thereby yielding a net energy gain of ~0.48 (0.30) W m 2 at the surface (Table 6); all models that include the oceanic heat transport variable exhibit decreased meridional ocean heat transport under the A1B scenario. Surface warming and increased longwave radiation leaving the surface are maintained primarily by decreased ocean heat transport. In addition, decreased atmospheric energy transport results in an energy surplus of ~4.18 (2.99) W m 2 in the tropics, thereby leaving a net energy gain of ~5.82 (3.23) W m 2 in the atmospheric column (Table 8). Note that energy transport by transient and stationary eddies is not considered in the present study since it cannot be estimated accurately from the monthly datasets. Energy transport change by eddies may not necessarily be small and is an important source of uncertainty in closing the energy budget in the tropics. [33] Atmospheric energy transport is much larger than oceanic heat transport. The models, however, deviate significantly in terms of the magnitude of atmospheric energy transport with a standard deviation of 2.99 W m 2. On average, transport of internal energy (3.29 W m 2 ) explains about 3 times more energy gain in the tropics than transport of potential energy (1.00 W m 2 ) although the sign and the ratio of the two vary significantly from one model to another. All 15 models consistently show negative anomaly of energy transport; thus, the reduction of atmospheric meridional energy transport contributes to the heating of the tropical atmosphere although the magnitude of warming cannot be ascertained because of the large model uncertainty (Table 8). This change in the meridional transport of static energy implies that the energy budget in the mid-latitudes and potentially the polar region are linked with the change in the tropical energy budget associated with the increasing concentration of greenhouse gases. 5. Summary and Discussion [34] In the present study, climate change signals were extracted via CSEOF analysis from 15 IPCC AR4 model data sets that are associated with the increased concentration of greenhouse gases according to the SRES A1B scenario. The climate change signals were explicitly extracted from the IPCC AR4 model datasets with individual terms for the detailed energy budget changes in the tropics (Figure 7); this approach is in contrast to the conventional approach, which is prone to contamination by natural variability. After separating the climate change signal from natural variability, we were able to investigate thoroughly the pattern of energy budget changes in the tropics as a function of climate change. [35] At the top of the atmosphere, outgoing longwave radiation increased. This increase was due to atmospheric warming and the decreased cloud fraction over most of the tropics. Owing to the decreased cloud fraction, reflected shortwave radiation decreased. There was a net increase of W m 2 for radiation over 100 years at the top of the atmosphere, as the decrease of reflected shortwave radiation is greater than the increase of the outgoing longwave radiation. [36] In the atmospheric column, the exchange of downward and upward longwave radiation with the surface increased by ~19.22 W m 2 and ~14.50 W m 2, respectively. This increase is related to the increased greenhouse gases and water vapor, both of which absorb longwave radiation. Specific humidity, in particular, increased significantly over the equatorial Pacific. Greater increases were found for longwave radiation emission by the atmosphere relative to emission by the surface. There was a positive vertical energy budget of W m 2 in the atmospheric column, due to increased absorption of shortwave radiation and increased heat fluxes from the surface. The energy transport in the atmospheric column was, on average, W m 2,which is much larger than the vertical energy budget. This net energy surplus ( W m 2 ) results in atmospheric warming in the tropics. Lack of information on the energy transport by eddies is an important source of uncertainty in closing the energy balance in the tropics. [37] At the surface, net longwave radiation exchange with the atmosphere provided an energy surplus. Latent heat flux from the surface, however, increased significantly, primarily due to increased evaporation at the surface [Peixoto and Oort, 1992]. Decreased absorption of solar radiation and increased latent heat flux led to a vertical energy deficit of W m 2 at the surface. Given the ocean heat transport of W m 2, there was a net energy increase of

13 0.30 W m 2. The convergence of heat in the tropical oceans is due to a weakening of ocean heat transport to higher latitudes. [38] Thus, there was a net surplus of energy at each level of the tropical climate system according to the SRES A1B scenario. The most noteworthy feature of the energy budget change in the tropics was a substantial increase in the exchange of longwave radiation between the surface and the atmospheric column. Increased longwave radiation from the surface and from the atmospheric column over 100 years amounts to approximately 3 5% of the mean values; this increase represents the most prominent increase in the vertical energy budget. This phenomenon is related to positive water vapor feedback [Philipona et al., 2005; Dessler and Sherwood, 2009], as described below. As the anthropogenic greenhouse gases (e.g., carbon dioxide) increase, surface temperature also increases, and this increased surface temperature accelerates evapotranspiration such that water vapor becomes more abundant in the troposphere. Because water vapor is a notably effective greenhouse gas, and the anthropogenic greenhouse gas emission persists, surface temperature increases further. This procedure repeats until the temperatures of the surface and the troposphere reach equilibrium. [39] A significant increase in the longwave radiation exchange between the surface and the atmosphere together with the surface flux increase, which amounts to approximately 3% of the mean turbulent flux, means increased variability in the physical processes both in the vertical and horizontal directions. With increased concentration of greenhouse gases, advection, convection, and instability of the atmospheric column, particularly the lower atmosphere, may exhibit stronger variability than the present climate. Increased specific humidity in the lower troposphere amplifies the atmospheric variability since moisture is a powerful source of energy in the atmosphere. [40] The points (1) (3) above are relevant to previous research on climate changes in the twentieth century [Wielicki et al., 2002; Wild et al., 2008; Andronova et al., 2009]. Wielicki et al. [2002] and Andronova et al. [2009] used observational satellite data to investigate the tropical mean TOA radiative energy budget. Wielicki et al. [2002] and Andronova et al. [2009] showed that the TOA-reflected shortwave radiation decreases and outgoing longwave radiation increases in the tropics. These results are generally consistent with the findings of the present study, which used the AR4 model datasets, implying that the climate change trend in the late twentieth century will probably persist throughout the 21st century. The earlier studies, however, contained a slightly larger amount of variation than the present study. Further, Wild et al. [2008], who investigated the surface radiation budget using the Global Energy Balance Archive (GEBA) database, showed that the net surplus of energy at the land surface was due to increased downward solar radiation and downward longwave radiation. This study, on the other hand, estimates a net deficit of energy at the surface, although it represents both the ocean and land and is small and within the model spread. At present, it appears that the AR4 model simulations are somewhat inaccurate, quantitatively. [41] An obvious limitation of this study is the uncertainty inherent in the IPCC AR4 models. 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