An update on Earth s energy balance in light of the latest global observations

Transcription

1 SUPPLEMENTARY INFORMATION DOI: /NGEO1580 An update on Earth s energy balance in light of the latest global observations Graeme L. Stephens, Juilin Li, Martin Wild, Carol Anne Clayson, Norman Loeb, Seiji Kato, Tristan L Ecuyer, Paul The W. values Stackhouse of the Jr, Mathew incoming Lebsock solar and irradiance Timothy Andrews at the TOA comes from NASA s Total Irradiance Monitor (TIM) measurements 1. The outgoing TOA fluxes summarized on Fig.1 are an average of the Energy Budget adjusted fluxes (EBAF) 2 edition 2.6r listed in Table S.1. Also provided for comparison are the unadjusted fluxes from the CERES_SYN1deg_lite_Ed2.6 products that include the latest instrument calibration improvements, algorithm enhancements and other updates. The source of uncertainty on CERES fluxes includes random calibration errors that are 2% for reflected shortwave fluxes (±2 Wm -2 ) and 1% for longwave fluxes (±1 Wm -2 ) at night. Daytime longwave fluxes also have to account for uncertainties in both the measured shortwave and total (long-plus-short-wave) fluxes producing a 24 hour average error in longwave flux of ±3.3 Wm -2. The ±4.1 Wm -2 net flux error (rounded to 4Wm -2 ) follows as a combination of longwave and shortwave flux errors. Absolute calibration uncertainty also contains a bias of unknown sign but in tracking changes in fluxes over time with related ocean heat content data it appears that this bias is unchanging in time Table S1. Summary of TOA fluxes used to produce the averaged outgoing TOA fluxes and the clear-cloudy sky flux differences in Fig. 1 EBAF Ed2.6r January 2006 December 2010 Terra SYN1deg Ed2.6 Aqua SYN1deg Ed2.6 NATURE GEOSCIENCE 1

2 Incoming Solar LW (all-sky) SW (all-sky) Net (all-sky) LW (clear-sky) SW (clear-sky) Net (clear-sky) EBAF Ed2.6r March 2000 February 2010 Terra SYN1deg Ed2.6 Incoming Solar LW (all-sky) SW (all-sky) Net (all-sky) LW (clear-sky) SW (clear-sky) Net (clear-sky) Surface radiative flux data The different estimates of surface radiation fluxes used to construct Fig. 1 are summarized in Table S.2. The flux uncertainty is in part inferred from direct comparison to surface observations and a significant component of this error arises from the random effects of sampling error resulting from the basic different between point - wise surface observations and aerial mean satellite observations. Given the large amount of data that goes into these different global averaged products, this random sampling error will largely disappear and the overall uncertainty in the global values are smaller than uncertainties derived from comparison from surface measurements at specific monitoring sites. 33 The sources of surface radiation fluxes include: 2

3 a) The NASA/GEWEX SRB project ( srb.larc.nasa.gov) 3 that provides a long- term record of global gridded datasets for shortwave and longwave surface including TOA fluxes from July 1983 to December These fluxes are derived using cloud and surface reflectance measurements and cloud radiative properties provided by the International Satellite Cloud Climatology Project DX 4 data set. Temperature and water vapor profiles also required are taken from the 6- hourly 4- D data assimilation products provided by the Global Modeling and Assimilation Office (GMAO) and produced using Goddard Earth Observing System reanalysis (GEOS4) 5. The fluxes have been extensively compared to the Baseline Surface Radiation Network (BSRN) 6 surface observations and these comparisons indicate that the mean biases are well within the uncertainty of the surface measurements (i.e., within 5 Wm - 2 ) and random errors that arise from a variety of factors range between ±5 to 20 Wm b) ISCCP- FD fluxes are from the Zhang et al 7 and are calculated from an advanced radiation scheme using ISCCP D1 input data that includes global observations of the key variables. An 18- year flux record at 3- hour time steps, global at 280- km intervals has been created. Based on comparisons of monthly, regional mean values to the Earth Radiation Budget Experiment (ERBE) and the Clouds and the Earth's Radiant Energy System (CERES) TOA fluxes and to BSRN surface fluxes, Zhang et al conclude that the overall uncertainties are 5 10 Wm - 2 at TOA and Wm - 2 for the surface longwave fluxes and 10-20Wm - 2 for shortwave fluxes. Comparisons to BSRN also suggest biases in monthly shortwave fluxes are less than 5Wm

4 c) Surface fluxes from the Clouds and the Earth s Radiant Energy System (CERES) Ed2 AVG product are estimated using data from January 2001 through December The method of the flux calculations in AVG is given in Kato et al. 8 Briefly, inputs for the AVG flux computations include: 6- hourly temperature and humidity profiles and 3- hourly skin temperature from GEOS4, and MODIS and 3- hourly geostationary satellites- derived cloud properties 9 to account for the diurnal cycle. The estimated surface shortwave flux error is ± 6 Wm - 2 for the version constrained to the TOA CERES measured reflected flux d) Two different estimates of surface fluxes based on the use of CloudSat and other A- train observations of cloudiness and atmospheric state parameters. These A- Train fluxes are especially noteworthy because unlike the other estimates given in Table S2, the flux values quoted are based on actual cloud profile observations, notably including the critical new information about cloud base derived from CloudSat and CALIPSO 10. Cloud base information is one of the important parameters needed to determine the surface longwave fluxes under cloudy conditions One A- Train flux product uses the updated version of the 2B- FLXHR product 12 that includes improved depictions of clouds through the combination of lidar and radar observations 13. Vertical distributions of liquid and ice cloud water contents and effective radii from the level- 2 cloud water content product (2B- CWC) are combined with ancillary temperature and humidity profiles from the European Centre for Medium- range Weather Forecasts (ECMWF) analyses and surface albedo and emissivity data from the International Geosphere- Biosphere Programme (IGBP) 4

5 global land surface classification to initialize a two- stream, doubling- adding broadband radiative transfer model. A noteworthy difference between this product and others like it is the radiative effects of precipitation are explicitly included using estimates of rainfall rate and the height of the raining column from the CloudSat 2C- PRECIP- COLUMN product 14. The updated version of 2B- FLXHR includes aerosols with optical properties derived from the Spectral Radiation- Transport Model for Aerosol Species (SPRINTARS) global aerosol transport model 15 adjusted to match CALIPSO aerosol information. Sensitivity analysis and propagation of errors produces similar flux errors as those deduced for the other products The second A- Train flux product is that of Kato et al 7. This product also uses Cloudsat radar and CALIPSO lidar profile data as well as other A- Train data including cloud properties taken from MODIS observations using the a CERES cloud algorithm. Temperature and humidity profiles used in the computations were from the Goddard Earth Observing System (EOS) Data Assimilation System reanalysis (GEOS- 5) The longwave flux errors range from about 6-15 Wm - 2 with the main source of error arising from uncertainties in temperature and water vapor information that is required to derived the fluxes 7,11. Biases in the estimates are determined to be within the accuracy of the surface observation used to assess them. Similarly the downward solar flux bias is less than 5 Wm - 2 but the solar flux errors attached to the different summarized in Table S2 range between 6-20 Wm

6 Table S2 Summary of all-sky and clear sky global, annual mean surface radiation fluxes. Uncertainties given as +/- are determined from surface measurement validation and from global average sensitivity studies. 103 All Sky All Sky LW up LW dn LW net SW up SW dn SW net GEWEX SRB (1/84 12/07) Primary Algorithm ± ± ISCCP - FD ±10/ ± Wild et al ±>5* CERES (Ed2 AVG) A- Train Radar+Lidar (L) 398±9 350±9-48± Radar+Lidar (CCCM) 398± ±7-51± Sensible Heat Fluxes The SeaFlux 16 version 1.0 dataset is the basis of sensible heat flux over oceans and the LandFlux product (mean of 12 different land products) is used for the sensible heat flux over land 17. New advances in estimating surface- air temperature differences are used, as well as winds from the CCMP dataset 18 and an updated 6

7 version a flux parameterization 19 to determine the air- sea turbulent fluxes of latent and sensible heat flux. These data are currently available from 1998 through 2007, and the ocean mean and uncertainty of 17±15 Wm - 2 are calculated for this time period. The land average sensible heat fluxes are 45±15 Wm - 2 where the uncertainty in this case reflects the range in the different land flux products Precipitation and Latent heat flux The latent heat flux inferred from global precipitation derived from the Global Precipitation Climatology Project (GPCP) 20 is 76 Wm - 2. This value is adjusted upward for the following reasons: (i) It is now possible to estimate the contribution by snowfall that has been previously ignored. CloudSat radar has the sensitivity needed to detect the occurrence of precipitation of all phases and is now providing the first global surveys of snowfall 21. Snowing pixels in the observations are identified using the procedures contained in the snow certain determination in the 2C- Precip- Column (Release 05) product 12. Once detected, snowfall intensity (S) is estimated using the reflectivity (Z) in the sixth range bin above the surface (~1300 m) using a straightforward Z- S relationship of the form Z = as!. The intensity data from four years ( ) of CloudSat observations are accumulated into 1 latitude/longitude bins, which are subsequently aggregated to annual and 4- year means. Weighting these bins by their respective areas and integrating over the Earth surface results in a global mean snowfall rate. The global mean snowfall rate is then multiplied by the latent heat of fusion (334 kj/kg) and the latent heat of 7

8 vaporization (2260 kj/kg) to arrive at the flux. The global mean latent heating of the atmosphere of 3.9 Wm - 2 with +/- 1σ uncertainty bounds of ( Wm - 2 ). There are a number of complicating factors that make these estimates uncertain but the main uncertainty results from the influence of different crystal habits and particle size distributions on the radar reflectivity (ii) The remote sensing methods widely used to determine the oceanic precipitation contained in GPCP are likely to be biased low by about 10% due to missing contributions from light rain. The study of Berg et al 22 provide guidance on the magnitude of the underestimate quantify this underestimate in tropical regions by comparing CloudSat observations matched to observations from the Tropical Rainfall Measurement Mission (TRMM). Cloudsat observations also suggest previous estimates of precipitation over mid- latitude ocean regions are too low by factors that are not yet fully known. 12,23 Petty 24 also concluded the satellite estimated of precipitation based on use of microwave radiometer data that form the bases of most oceanic precipitation estimates substantially underestimate the occurrence of mid- latitude precipitation compared to ship- borne surface observations. The exact amount of underestimate is not known precisely and CloudSat observations suggest it may even exceed 10% Model data and analysis methods The model data reported are taken from two different climate experiments archived under the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project phase 5 (CMIP5). Data from the historical experiments 8

9 from 16 different models for a set of forcing conditions that represent approximately the decade of are used to produce the comparison shown in Fig The CMIP5 multi- model data archives also include output from climate models forced by a 1% per year increase in CO2, amongst other scenarios of change. Data from 12 different models forced by this scenario are individually presented in Fig. 3 as well as the multi- model mean flux changes. The projected global warming induced by this increase varies from model to model according to how different feedbacks and heat uptake operate in each respective model. This range of warming is exemplified in Fig. S1, which shows the change in global- mean surface- air- temperature averaged over years (difference calculated by subtracting a corresponding linear fit from the pre- industrial control integration) for 10 of these models. Global mean temperature changes calculated in this way are referred to the Transient Climate Response (TCR) 25 and are used as a way of comparing fully coupled climate model responses to external forcing A convenient way to compare the small flux changes between models that possess very different climate sensitivities is to compare the sensitivities of fluxes to a given amount of warming. The small perturbations to the global, annual mean energy fluxes induced by the 1% per year increase in CO2 are approximately linear with respect to the global mean change of surface air temperature as shown in Fig S2 which highlights the response of 6 of these models. Because of this linearity, the slopes of such relationships (in Wm - 2 K - 1 ) provide a robust measure of the flux 9

10 sensitivities and effectively normalizes the effects of the different amounts of global warming in the different models Figure S1 The change in global decadal mean surface air temperature (relative to a control climate) derived from 10 different model simulations forced by a 1% per year increase in CO2. The simulations are from CMIP5 experiments that were integrated for 150 years. The change in global- mean surface- air- temperature was obtained as from the averaged over years (difference calculated by subtracting a corresponding linear fit from the pre- industrial control integration). The temperature differences calculated this way are referred to as the Transient Climate Response (TCR) and are used as the leading order metric for quantifying and comparing fully coupled climate model responses to external forcing

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12 Figure S2 CMIP5 climate model simulated changes to TOA (a,b) and surface (c,d) radiative fluxes as a function of changing surface air temperature Ta forced by a 1% per year increase in CO2. The changes are shown for 6 of the 12 models summarized in Fig 3. The flux changes are linear with respect to the change in Ta and the slopes of these linear relationships define the sensitivities summarized in Fig References 1 Kopp, G., Lawrence G., & Rottman G. The Total Irradiance Monitor (TIM): Science results. Solar Phys., 230, (2005). 2 Loeb,N., Wielicki B.A., Doelling D.R., Smith G.L, Keyes D., Kata S., Manalo- Smith N., & Wong T. Toward Optimal Closure of the Earth s Top- of- Atmosphere Radiation Budget. J Climate, 22, (2009). 12

13 Stackhouse, P. W., Jr., Gupta S. K., Cox S.J., Zhang T., J Mikovitz J.C., & Hinkelman L.M. The NASA/GEWEX Surface Radiation Budget Release 3.0: Year Dataset. GEWEX News, V 21, No. 1, February (2011). 4 Rossow W.D. & Schiffer R. Advances in understanding clouds from ISCCP. Bull. of the Amer. Meteor. Soc., 80, 11, p (1999). 5 Bloom, S. A., da Silva A., Dee D., Bosilovich M., Chern J- D, Pawson S., Schubert S., Sienkiewicz M., Stanier I., Tan W- W., & M- L. Wu M- L. Documentation and validation of the Goddard Earth Observing System (GEOS) data assimilation system version- 4, Technical Report Series on Global Modeling and Data Assimilation, , 26 (2005). 6 Ohmura, A., and coauthors, Baseline Surface Radiation Network (BSRN/WCRP), a new precision radiometry for climate research. Bull. Am. Meteorol. Soc. 79, (1998). 7 Zhang, Y- C., Rossow, W. B., Lacis, A. A., Oinas, V. & Mishchenko, M. I. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res., 109, D19105, doi: /2003jd (2004). 8 Kato, S., Rose F. G., Sun- Mack S., Miller W. F., Chen Y., Rutan, D. A. Stephens G. L., Loeb N. G., Minnis P., Wielicki, B. A., Winker D. M., Charlock T. P., Xu K.- M., & Collins W. Computation of top- of- atmosphere and surface irradiance with CALIPSO, CloudSat, and MODIS- derived cloud and aerosol properties, submitted to J. Geophys Res.,116, D19209, doi: /2011jd (2011). 9 Minnis, P. and coauthors, 2012: CERES Edition- 2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data, Part I: Algorithms. Submitted to IEEE Trans. Geosci. Remote Sens. 10 Mace G.G., Zhang Q., Vaughn M., Marchand R., Stephens G.L., Trepte C. & Winker D. A Description of Hydrometeor Layer Occurrence Statistics Derived from the First Year of Merged CloudSat and CALIPSO data. J. Geophys. Res., 114, doi: /2007jd (2009). 11 Stephens G.L, Wild M., Stackhouse Jr P. W., L Ecuyer T. 4, Kato S. 3 & Hendersen D. S. The global character of the flux of downward longwave radiation, J. Climate, 25, (2012). 12 L'Ecuyer, T.S., Wood N. B., Haladay T., Stephens G. L., & Stackhouse Jr P.W. Impact of clouds on the atmospheric heating based on the R04 CloudSat fluxes and heating 13

14 rates data set. J. Geophys. Res., 113, doi: /2008jd (2008). 13 Henderson, D., L'Ecuyer T. S., & Stephens G. L. A Multi- Sensor Approach to Assessing the Impacts of Clouds and Aerosols on Regional Radiation Budgets, submitted to J. Appl..Meteorol.Clim. (2012) 14 Haynes, J.M., L Ecuyer T.S., Stephens G.L., Miller S.D., Mitrescu C., Wood N.B. & Tanelli S., Rainfall retrieval over the ocean with spaceborne W- band radar. J. Geophys. Res., 114, doi: /2008jd (2009). 15 Takemura, T., Okamoto H., Maruyama Y., Numaguti Y., Higurashi A., & T. Nakajima T. Global three- dimensional simultion of aerosol optical thickness distribution of various origins. J. Geophys. Res., 105, (2000). 16 Clayson, C. A., Roberts J. B., & Bogdanoff A. S., Seaflux Turbulent flux data set (in preparation), (2012). 17 Jimenez C. & co- authors Global intercomparison of 12 land surface heat flux estimates, J Geophys.Res., 116,D02102, doi: /2010jd (2011). 18 Atlas, R., Hoffman R. N., Bloom S. C., Jusem J. C., & Ardizzone J. A multiyear global surface wind velocity data set using SSM/I wind observations. Bull. Amer. Meteor. Soc., 77, (1996). 19 Clayson, C.A., Fairall C.W. & Curry J. A., Evaluation of turbulent fluxes at the ocean surface using surface renewal theory. J. Geophys. Res., 101, 28,503-28,513(1996). 20 Adler, R.F., Huffman G.J., Chang A., Ferraro R., Xie P- P., Janowiak J., Rudolf B., Schneider U., Curtis S., Bolvin D., Gruber A., Susskind J., Arkin P. & Nelkin E. The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979- Present). J. Hydrometeor, 4, (2003). 21 Liu, G. Deriving snow cloud characteristics from CloudSat observations. J. Geophys. Res., 113, D00A09, doi: /2007jd (2008). 22 Berg, W., L Ecuyer T., and Haynes J. M. The distribution of rainfall over oceans from space- borne radars, J. Appl. Met and Climatol., 49, (2010). 23 Dai, A., Lin X., & Hsu K.- L. The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid- latitudes. Climate Dynamics, 29, , DOI /s y, (2007)

15 Petty, G. W. An inter- comparison of oceanic precipitation frequencies from 10 special sensor microwave/imager rain rate algorithms and shipboard present weather reports, J. Geophys. Res., 102, (1997). 25 Cubasch, U., Meehl G. A., Boer G. J., Stouffer R. J., Dix M., Noda A., Senior C. A., Raper S. & Yap K. S., Projections of future climate change. In: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. van der Linden, X. Dai, K. Maskell, C. I. Johnson (eds.)]. Cambridge University Press, ISBN (2001)