Influences of anthropogenic and oceanic forcing on top-of-atmosphere radiative fluxes

Transcription

1 Influences of anthropogenic and oceanic forcing on top-of-atmosphere radiative fluxes BRUCE T. ANDERSON Department of Geography and Environment, Boston University Boston, MA CLARA DESER AND ADAM PHILLIPS National Center for Atmospheric Research Boulder, CO 80302

2 ABSTRACT Anthropogenic climate change may be forced by changes in the atmospheric chemical composition directly, as well as indirectly via related changes in sea surface temperatures and sea ice. Ensemble integrations with the National Center for Atmospheric Research Community Atmospheric Model Version 3 (CAM3) forced by both historical changes in sea-surface temperatures (SSTs) and radiatively-active chemical constituents (primarily associated with increasing greenhouse gases and aerosols) are shown to be more realistic in terms of their trends in top-of-atmosphere radiative fluxes compared to those forced by sea-surface temperatures alone. In particular, the simulations with time-varying chemical composition and SSTs exhibit a decreasing trend in outgoing longwave radiation (-0.22 Wm -2 per decade), consistent with observational estimates, while those with only historical changes in SSTs exhibit an increasing trend in outgoing longwave radiation (+0.17 Wm -2 per decade). These results suggest that a signature of anthropogenic emissions direct effect upon the climate system can be identified within the observed top-of-atmosphere radiative flux data record. 2

3 1. Introduction Many recent studies have focused upon the detection and attribution of persistent surface and tropospheric warming over the last 35+ years. For a survey the reader is directed to IDAG (2005). These studies - using rigorous statistical and empirical methodologies to analyze historical changes in surface and upper-air temperatures, oceanic heat content, and regional variations in climate parameters - have shown that global trends in the observed climate are consistent with increasing radiatively-active atmospheric constituents resulting from increased anthropogenic emissions of greenhouse gases and aerosols. However, this conclusion is based in part upon the natural or internal variability within coupled climate model simulations, and in particular the variability of the simulated ocean state, as it compares with the historical evolution of observed values over recent decades. Although recent work indicates that the variability of the observed ocean system may actually be overestimated due to the sparse distribution of the observations (AchutaRao et al., 2006), on a globalscale the internal variability of ocean models is still approximately half of that observed, even when accounting for sub-sampling issues (see Figure 12 of AchutaRao et al., 2006). This discrepancy may be attributable to both trends and low-frequency variability in the observed data that are not present in control model simulations used to estimate internal variability (see Figure 11 of AchutaRao et al., 2006; Barnett et al., 2005). As pointed out by Folland et al. (1998) and Sexton et al., (2001), atmosphere-only general circulation models (AGCMs) can overcome the problems associated with underestimation of natural (or internal) coupled ocean/atmosphere variability because the sea-surface temperature (SST) evolution is specified from observations. In these runs, a control climate-change scenario is performed by integrating an AGCM forced by historical changes in SSTs and ice extent only. 3

4 These control simulations are then compared with AGCM simulations in which both historical changes in SSTs as well as in radiatively-active chemical constituents are included (Folland et al., 1998; Sexton et al., 2001; Brindley and Allan, 2003). In previous investigations of these AGCM simulations, fingerprint detection methods were applied to the simulated surface (Folland et al., 1998) and upper-air (Folland et al, 1998; Sexton et al., 2001; Bracco et al., 2004) globally- and zonally-averaged temperature fields to detect direct anthropogenic effects upon the climate, e.g., those climate changes that are forced directly by the inclusion of anthropogenic chemical constituents, separate from those that arise indirectly from the anthropogenic chemical constituents impact upon SST and sea-ice changes (Folland et al., 1998). These studies found that the direct anthropogenic effect upon surface temperatures is about half of the combined (i.e. direct and indirect) effect arising from coupled atmosphere-ocean interactions (Folland et al. 1998). Another metric for identifying climate variability induced by anthropogenic emissions are top-of-atmosphere (TOA) radiative fluxes, and out-going longwave radiation (OLR) in particular (e.g. Kiehl, 1983; Goody et al., 1996; Mitchell et al., 1987; Harries et al., 2001; Brindley and Allan, 2003). For AGCM simulations in which only the historical evolution of SSTs is prescribed, any input of energy by changing surface conditions at the bottom boundary of the atmosphere should be balanced by negative anomalies in globally-integrated, net-incoming radiation at the top of the atmosphere (Cess et al., 1990; Trenberth, 2002). In contrast, for AGCM simulations in which the historical increase in radiatively-active chemical constituents of the atmosphere is also prescribed, there will be a decrease in the TOA outgoing longwave radiation term (e.g. Kiehl, 1983; Mitchell et al., 1987). If not compensated for by a decrease in net incoming shortwave radiation (or a subsequent increase in OLR related to other atmospheric 4

5 processes), this decrease in OLR will translate into an increase in the net-incoming radiation at the top of the atmosphere compared with the SST-only AGCM simulations. In this study, we apply an AGCM methodology similar to Folland et al. (1998) but focus on TOA radiative fluxes instead of atmospheric temperatures as a means of distinguishing between SST influences vs. direct greenhouse-gas influences. Comparison to observed trends in TOA outgoing longwave and incoming net radiation is also made. This work is similar to that of Brindley and Allen (2003) who examined spectrally-resolved radiative fluxes and compared them with available observations for two discrete time periods (April 1970-January 1971 and October 1996-June 1997). In this study, we focus on the total (or integrated) radiative fluxes in order to use the longer and continuous observational record (July, 1974 Present) for comparison. Section 2 describes the data-sets used in this analysis. Section 3 shows the results from the atmospheric general circulation model experiments and compares them with available observations. Summary and conclusions are provided in Section Data For this study, we use output from atmosphere-only model simulations produced by the National Center for Atmospheric Research Community Atmosphere Model (CAM3.1) at T85 resolution (equivalent to 1.4 latitude x 1.4 longitude). Details of the physical and numerical methods used in CAM3.1 are provided in Collins et al. (2006). Analyses of the mean state and interannual variability in these simulations may be found in Hurrell et al. (2006), Hack et al. (2006) and Deser et al. (2006). Two 5-member ensemble simulations are examined: in the first, the model is forced only by historical changes in global SSTs during from the data set of Hurrell et al. (2006) (termed the AMIP simulation); in the second, the model is forced by the 5

6 same evolution of global SSTs plus historical changes in greenhouse gas (GHG) concentrations, sulfate aerosols, volcanic particulates, stratospheric and tropospheric ozone, and solar activity (termed the AMIP-ATM simulation). Different initial conditions are used for each member of the ensemble. To characterize changes in the overall earth/ocean/atmosphere energy balance, this study analyzes top of atmosphere outgoing longwave radiation and net incoming shortwave radiation (incoming minus outgoing) estimates from the CAM3.1 simulations. In addition we analyze the observed TOA OLR taken from the NOAA interpolated OLR dataset (Liebmann and Smith, 1996 termed the NOAA dataset) and the Lucas et al. OLR dataset, which corrects for changes in equatorial crossing times and satellite transitions (Lucas, et al., 2005 termed the ECTcorrected dataset). Monthly-mean data are available for the period and for the NOAA and ECT-corrected datasets, respectively, with the exception of 1978 when there was a sensor failure during part of the year. We have also analyzed the newest version of the ERBE Wide Field of View (WFOV) nonscanner data (Wielicki et al., 2002; Wong et al. 2006). This version of the data (Edition 3, Revision 1) has been corrected for a change in the orbit altitude as well as instrument drift (Wong et al. 2006). Mean values are available for 60 N- 60 S for every 72-day period from except during 1994 when there was a sensor failure during part of the year (see: _sf_erbs_edition3.html). Observational estimates of global near-surface temperatures are taken from the Hadley Centre s Climate Research Unit (CRU) gridded combined land/marine data product (Jones et al., 1999; Brohan et al., 2006). 6

7 3. Results Figure 1 shows the globally-averaged outgoing longwave radiation and net incoming shortwave radiation from the ensemble-mean CAM3.1 AMIP and AMIP-ATM simulations. Trends for all TOA radiation estimates are given in Table 1. For both datasets a 48-month Bartlett (or Triangle) filter has been applied to the monthly mean values and the climatological means over the full time-periods have been removed. The anomaly fields are then area-weighted based upon latitude and averaged over the whole globe. For the AMIP simulations, the historical increase in global-scale SSTs imposes a net surface heat-flux upon the atmosphere, which is realized as an increase in OLR over the course of the simulation. In contrast, when changing atmospheric chemical constituents are also included in the AMIP-ATM simulation, the TOA OLR actually decreases over time, despite the increasing surface temperatures (see Fig. 4), indicating that the enhanced GHG concentrations are resulting in an increase in the absorption of longwave radiation within the atmosphere and a decrease in the emissions to outer-space. We provide error bars in this panel (designating the 95% confidence interval based upon the standard error of the mean); here we use them to quantify the spread of the ensemble members about the mean value in order to show that the mean values are representative of the ensemble members themselves and are not simply residuals between very disparate values. We do not show error bars in other figures because all fields indicate similar agreement between individual ensemble members and the ensemble mean. For the net incoming shortwave radiation, there is a slight decrease across the 50-year time period in the AMIP simulation (Table 1), possibly due to changes in cloud cover. In addition, when including aerosol concentrations into the AMIP-ATM simulation, there are significant 7

8 decreases in net solar radiation associated with the volcanic eruptions around 1964, 1982, and 1991, which enhance the negative trend (Table 1). However, it appears that by the end of the 50- year integration, inclusion of both the volcanic aerosols and anthropogenic aerosols have little effect upon the net incoming shortwave radiation term: for both simulations the difference between the average value over the first 4-year period and the last 4-year period is -0.23W/m 2. Figure 2 shows the globally-averaged net incoming TOA radiation from the ensemble-mean AMIP and AMIP-ATM simulations; trends for the datasets are provided in Table 1. As expected, the AMIP simulation, which does not contain explicit changes in the chemical composition of the atmosphere, indicates a net loss of radiative energy through the top of the atmosphere on the order of 1.09W/m 2 (estimated by differencing the average value over the first 4-year period and last 4-year period of the simulation). However, we see that for the AMIP-ATM simulations, there is an increase in the net incoming TOA radiation field of 0.47 W/m 2, indicating that there is a net retention of energy within the earth system despite the increase in SST fields. The differences in top-of-atmosphere radiation fluxes - OLR and net-incoming radiation - suggest that along with surface and atmospheric temperatures these fields can be used as additional discriminating metrics for differentiating between the historical evolution of the atmosphere with and without changes in radiatively-active chemical constituents. The difference in the net TOA radiative flux fields from the two simulations also gives an estimate of the total radiative forcing associated with the inclusion of these radiatively-active chemical constituents (Cess et al., Figure 3). This figure indicates that the change in total radiative forcing by the end of the simulation is approximately 0.47-(-1.09)=1.56 W/m 2. To show just the radiative forcing associated with changing GHG concentrations, this figure also plots the difference in net 8

9 TOA radiation between the AMIP and AMIP-ATM simulations when using the AMIP net incoming shortwave radiation for both. Doing so effectively removes the changes in net TOA radiation associated with changing reflectances, principally related to volcanic eruptions. As can be seen, there is a very steady, near-linear rise in the total radiative-forcing associated with just the changes in longwave radiation, i.e. the GHG radiative forcing over the last 50 years of the 20 th century. To see how observed estimates of the near-surface temperatures and TOA radiative fluxes compare with model simulations, Figure 4 shows the global-average surface temperatures as well as the satellite-based OLR estimates (taken from the NOAA-interpolated and ECT-corrected datasets). Trends for these estimates are also given in Table 1. For the CAM3.1 model systems the global average temperature change is ΔT s =0.35K and 0.43K for the AMIP and AMIP-ATM simulations respectively; for the observed system, the global average temperature change is ΔT s =0.43K. As discussed in previous studies (Folland et al., 1998; Sexton et al., 2001), the more realistic global surface temperature change found in the AMIP-ATM simulation indicates that a direct effect of anthropogenic emissions upon the observed climate system can be identified in the historical temperature record. We next examine the globally-averaged observed OLR data (Figure 4b). Trends in the available observed radiative terms are provided in Table 1; given the short duration of these observed fields and the size of the interannual variability, none of these trends are significant, however we present them here for qualitative comparison both with the model data and with one another. Although both NOAA-based OLR fields (NOAA and ECT-corrected OLR) show more interannual variability than the simulations (Allen and Slingo, 2002; Wielicki et al., 2002), trends in these two datasets match those in the AMIP-ATM simulations quite well both in terms of sign 9

10 and magnitude, and are inconsistent with the upward trend found in the AMIP simulation (Table 1). We have not included the ERBE Wide Field of View Nonscanner dataset because its short duration (10 years after time-averaging is applied) limits its use to investigations of higherfrequency climate variability (T. Wong; Personal communication). However recent studies indicate that the ERBE data show an overall increase in the net incoming radiation flux, in agreement with independent observations of ocean heat content over the same time-period (Wong et al., 2006). As with the NOAA-based OLR estimates, these results are qualitatively similar to the AMIP-ATM simulations but not the AMIP simulations (Table 1), again suggesting that observed changes of this metric indicate the influence of anthropogenic chemical constituents upon the global climate. 4. Conclusions Attribution of recent climate change to various influences has been systematically studied over the last two decades. Numerous results have shown that global trends in the observed climate are largely consistent with increasing radiatively-active atmospheric constituents resulting from increased anthropogenic emissions of greenhouse gases and aerosols (e.g. IDAG, 2005). One novel approach has been to compare output from atmosphere-only general circulation models forced by observed historical sea-surface temperatures with output from the same models forced by historical sea-surface temperatures as well as radiatively-active chemical constituents. This approach allows for detection of the direct effect of anthropogenic emissions upon tropospheric temperatures (Sexton et al., 2002; Bracco et al., 2004). While smaller than the fully coupled ocean-atmosphere response to these same emissions (Folland et al. 10

11 1998), this direct effect appears to be more detectable because variations in large-scale oceanic forcing are prescribed, thereby removing one possible source of noise within the coupled climate system. Here we provide a continuation of this effort by examining the changes in incoming and outgoing radiation at the top of the atmosphere (TOA) in an ensemble of AGCM runs using CAM3.1. We find that for the SST-only forced CAM3.1 simulations, the excess energy applied to the atmosphere via increased SSTs is effectively removed via increased top-of-atmosphere out-going longwave radiation, resulting in an anomalous decrease of the total net incoming radiation flux. In comparison, for the CAM3.1 simulations forced both with historical SSTs and historical changes in radiatively-active chemical constituents primarily associated with increasing greenhouse gases and aerosols the anomalous OLR leaving the top of the atmosphere switches from positive to negative, resulting in an anomalous increase in the net incoming radiation flux. We show here that the simulations forced with both historical changes in sea-surface temperatures and radiatively-active chemical constituents are more realistic than the ones forced by sea-surface temperatures alone, based upon observed values of top-ofatmosphere radiative quantities. These results suggest that the direct effect of anthropogenic emissions upon the observed climate system can be identified not just in historical temperature changes but in radiative fields as well. Acknowledgements: We thank Masao Kanamitsu, Katharine Hayhoe, and Helen Brindley for their insightful comments on drafts of this paper. We also thank Bruce Wielicki and Takmeng Wong for their help acquiring and analyzing the ERBE WFOV data. Interpolated OLR data provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, USA, from 11

12 their Web site at ECT-corrected OLR data provided by the UCAR/NCAR Data Support Section, Boulder, Colorado, USA, from their Web site at CAM3.1 data used in this study/project have been provided by UCAR/NCAR have been obtained from the Earth System Grid Data Server. ERBE WFOV OLR Edition3_Rev1 available from: 12

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16 TABLE LEGENDS TABLE 1 Trends in globally-averaged top-of-atmosphere radiative terms from model simulations and available observations: OLR outgoing longwave radiation; Net SW - net incoming shortwave radiation; Net Rad - total net radiation calculated as the difference between Net SW and OLR. Units are in (W/m 2 )/decade. Sign convention such that positive values are represented by the direction of arrow. For all data the time-series are smoothed using a 4-year Bartlett filter before calculating trends. Trends calculated using slope of best-fit linear interpolation for length of available record. Also shown is the change in globally-averaged near surface temperatures from the model simulations and the Climate Research Unit (CRU) observational estimates. Trends for surface temperatures given as difference between average value over first 4-year period and last 4-year period. 16

17 FIGURE CAPTIONS FIG. 1 (a) Globally-averaged change in outgoing longwave radiation (OLR) at the top of the atmosphere, taken from the CAM3.1 AMIP and AMIP-ATM simulations for the period OLR values are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the time-series is centered on 0. Dashed lines give the 95% confidence interval based upon the standard error of the mean value at each time-step. All values have units of W/m 2. (b) same as Figure 1b except for change in incoming net shortwave radiation. FIG. 2 Globally-averaged change in net incoming radiation at the top of the atmosphere, taken from the CAM3.1 AMIP and AMIP-ATM simulations for the period Net incoming radiation calculated as the difference between the net incoming shortwave radiation and outgoing longwave radiation. All values are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the time-series is centered on 0. All values have units of W/m 2. FIG. 3 Globally-averaged change in the difference between the net incoming TOA radiation from the CAM3.1 AMIP-ATM and AMIP simulations for the period (solid line). Also shown is the globally-averaged change in the difference between the net incoming TOA radiation from the CAM3.1 AMIP-ATM and AMIP simulations for the period when using the CAM3.1 AMIP net shortwave radiation for both (dashed line). All values are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the timeseries is centered on 0. All values have units of W/m 2. 17

18 FIG. 4 (a) Globally-averaged surface temperatures taken from the CAM3.1 AMIP and AMIP-ATM simulations and the Climate Research Unit (CRU) observations. For all data, timeseries are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the time-series is centered on 0. (b) Same as Figure 4a except for top-of-atmosphere outgoing longwave radiation (OLR). OLR estimates taken from the Equatorial Crossing Time (ECT) corrected data for the period (with 1978 anomalies set to 0), the interpolated NOAA OLR for the period (with 1978 anomalies set to 0), and the CAM3.1 AMIP and AMIP-ATM simulations. For all data, time-series are smoothed using a 4-year Bartlett filter and plotted at the center of the averaging interval. Thick lines represent linear trends in respective time-series. 18

19 TABLE 1 Trends in globally-averaged top-of-atmosphere radiative terms from model simulations and available observations: OLR outgoing longwave radiation; Net SW - net incoming shortwave radiation; Net Rad - total net radiation calculated as the difference between Net SW and OLR. Units are in (W/m 2 )/decade. Sign convention such that positive values are represented by the direction of arrow. For all data the time-series are smoothed using a 4-year Bartlett filter before calculating trends. Trends calculated using slope of best-fit linear interpolation for length of available record. Also shown is the change in globally-averaged near surface temperatures from the model simulations and the Climate Research Unit (CRU) observational estimates. Trends for surface temperatures given as difference between average value over first 4-year period and last 4-year period. Name Duration OLR( ) Net SW( ) Net Rad( ) Sfc. Temp. (per decade) (per decade) (per decade) (total) CAM3.1 AMIP CAM3.1 AMIP- ATM NOAA ECT-Corrected CRU

20 (a) (b) FIG. 1 (a) Globally-averaged change in outgoing longwave radiation (OLR) at the top of the atmosphere, taken from the CAM3.1 AMIP and AMIP-ATM simulations for the period OLR values are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the time-series is centered on 0. Dashed lines give the 95% confidence interval based upon the standard error of the mean value at each time-step. All values have units of W/m 2. (b) same as Figure 1b except for change in incoming net shortwave radiation. 20

21 FIG. 2 Globally-averaged change in net incoming radiation at the top of the atmosphere, taken from the CAM3.1 AMIP and AMIP-ATM simulations for the period Net incoming radiation calculated as the difference between the net incoming shortwave radiation and outgoing longwave radiation. All values are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the time-series is centered on 0. All values have units of W/m 2. 21

22 FIG. 3 Globally-averaged change in the difference between the net incoming TOA radiation from the CAM3.1 AMIP-ATM and AMIP simulations for the period (solid line). Also shown is the globally-averaged change in the difference between the net incoming TOA radiation from the CAM3.1 AMIP-ATM and AMIP simulations for the period when using the CAM3.1 AMIP net shortwave radiation for both (dashed line). All values are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the timeseries is centered on 0. All values have units of W/m 2. 22

23 (a) (b) FIG. 4 (a) Globally-averaged surface temperatures taken from the CAM3.1 AMIP and AMIP- ATM simulations and the Climate Research Unit (CRU) observations. For all data, time-series are smoothed using a 4-year Bartlett filter. Data plotted such that the 4-year period at the beginning of the time-series is centered on 0. (b) Same as Figure 4a except for top-of-atmosphere outgoing longwave radiation (OLR). OLR estimates taken from the Equatorial Crossing Time (ECT) corrected data for the period (with 1978 anomalies set to 0), the interpolated NOAA OLR for the period (with 1978 anomalies set to 0), and the CAM3.1 AMIP and AMIP-ATM simulations. For all data, time-series are smoothed using a 4-year Bartlett filter and plotted at the center of the averaging interval. Thick lines represent linear trends in respective time-series. 23