The Curious Nature of the Hemispheric Symmetry of the Earth’s Water and Energy Balances
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This paper presents new estimates of the hemispheric energy balance based on an assembly of radiative flux and ocean heat data. Further, it provides an overview of recent simulations with fully coupled climate models to investigate the role of its representation in causing tropical precipitation biases. The energy balance portrayed here features a small hemispheric imbalance with slightly more energy being absorbed by the Southern hemisphere. This yields a net transport of heat towards the NH composing of a northward cross-equatorial heat transport by the oceans and a southward heat flow in the atmosphere. The turbulent fluxes and hemispheric precipitation balance to about 3 Wm−2 with slightly larger total accumulation occurring in the NH. CloudSat data indicate more frequent precipitation in the SH implying more intense precipitation in the NH. Fully coupled climate model simulations show that reducing hemispheric energy balance biases does little to reduce existing biases in tropical precipitation.
KeywordsHemispheric energy balance Surface heat budget Ocean heat storage Cross-equatorial heat transport Fully coupled climate model Tropical precipitation bias
The Earth’s climate is determined by the flows (fluxes) of energy into and out of the planet and to and from the Earth’s surface. The geographical distributions of these fluxes are particularly important as the latitudinal variation of the top of the atmosphere (TOA) fluxes establishes one of the most fundamental aspects of Earth’s climate determining how much heat is transported from low latitudes to high latitudes (e.g., [16, 66, 74] among many others). The further disposition of net TOA energy between the atmosphere and surface is also of fundamental relevance to climate (e.g., [22, 23]). The surface energy fluxes are fundamental to understanding the carbon, energy, and water nexus. Surface energy fluxes drive ocean circulations, determine how much water is evaporated from the Earth’s surface, and govern the planetary hydrological cycle (e.g., ). Changes to the surface energy balance also ultimately control how this hydrological cycle responds to the small energy imbalances that force climate change [2, 3, 51, 60].
Despite the fundamental importance of the energy balance to our understanding of climate and climate change, there still remain a number of challenges in quantifying it globally and in understanding its behavior regionally. Current depictions of the global surface energy balance (e.g., [41, 62, 63, 64]) indicate that uncertainties attached to our best depiction of the net surface energy balance are an order of magnitude larger than the small imbalance of approximately 0.6 ± 0.43 Wm−2 that is inferred to exist from the ocean heat content (OHC) changes (e.g.,  among several others). Although the uncertainty in TOA fluxes is also larger than this inferred imbalance, both Wong et al.  and later Loeb et al.  demonstrated that changes to TOA net fluxes generally track the changes in OHC suggesting TOA flux balances could be tuned to OHC changes. It is for this reason that Loeb et al.  apply the global values of OHC as a constraint on the global TOA radiation balance. Nonetheless, Trenberth et al.  note that there are still important discrepancies between OHC data and the TOA net flux observations, thus suggesting that even this constraint approach has significant uncertainties with respect to closing the TOA energy balance. No equivalent data record exists for the net energy balance at the surface and no attempt has been made to examine the extent that the global surface energy balance changes also track OHC changes.
Recently, considerable interest has been directed towards understanding the more regional changes in energy balance and much of this interest has developed around the study of the hemispheric energy balances. Hemispheric energy balances have been invoked to explain the northern annual average location of the intertropical convergence zone (ITCZ) (e.g., [20, 30, 31, 49]) and to evaluate the realism of climate models using observations ([17, 28, 65]). The properties of this hemispheric balance are updated and reviewed in this study. The various sources of data used in this update are summarized in the next section. Because energy and water are synonymous within the Earth system, the present study also goes beyond the energy balance viewpoint and discusses the hydrological cycle on a hemispheric scale in “The Hemispheric Energy Balance”. The impact of a hemispherically symmetric energy balance is explored in “Symmetry in Earth System Models (ESMs)” in the form of model experiments that apply adjustments to make the solar reflection approximately symmetric. A series of recent global Earth-system model studies [24, 25, 36] have highlighted the acute sensitivity of Earth’s climate and hydrological cycle to the degree to which the hemispheric albedo is symmetric and highlights from these studies are reviewed in “Symmetry in Earth System Models (ESMs)”. Consistent with observations, these modeling studies underscore the importance of advancing dynamical understanding using modeling frameworks that represent the fully coupled and dynamic global atmosphere and ocean circulations.
Data and Methods
Ocean Heat Content
The ocean heat content (OHC) is commonly computed by vertically integrating the ocean temperature profiles multiplied by the density and specific heat of sea water [4, 43]. Three different sources of quality controlled and gridded temperatures are used: The National Oceanic and Atmospheric Administration (NOAA, ), the Scripps Institution of Oceanography , and the International Pacific Research Center (IPRC). These datasets can be downloaded at www.argo.ucsd.edu/Gridded_fields.html and www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/. While the Scripps and IPRC datasets combine only Argo float data, the NOAA dataset also includes other in situ measurements, such as expendable bathythermograph (XBT), conductivity, temperature, and depth (CTD) instruments, and mooring data. These datasets contain ocean measurements above 2,000 m depth over the period 2005–2015. The ocean heat storage (OHS) of the last decade was derived from these data after applying robust regression (using iteratively reweighted least squares) to the global and hemispheric OHC time series.
Cross Equatorial Ocean Heat Transport
A cross-equatorial heat transport exists as a consequence of the differential ocean heat content between hemispheres. We make use of various global ocean reanalysis and observation-based surface heat flux (F s) datasets, which, integrated meridionally and zonally, depict the long-term averaged meridional heat transport. Our main foci are the ECCOv4  and GECCO2 (Köhl, 38) datasets that are based on the same model (MITgcm), but employ different adjusted forcing fields and bulk formulae. Within the Ocean Reanalysis Intercomparison Project (ORA-IP, ), these datasets are shown to be least affected by interior sources and sinks of heat associated with the temperature assimilation and exhibit nearly closed global budgets . For comparison, we include six other datasets, namely the Global Ocean Data Assimilation System Global Ocean Physics Reanalysis C- GLORS , the NCEP Global Ocean Data Assimilation System (GODAS, ), the CORE.2 Global Air-Sea Flux Dataset , the satellite-based J-OFURO2 dataset , the NOCS Surface Flux Dataset v2.0 based on ICOADS v2.4 ship data, and the WHOI Objectively Analyzed air-sea Fluxes (OAFlux, ). Detailed specifics and a comparison of these data products are provided in Balmaseda et al.  and Valdivieso et al. .
The oceanic meridional heat transport derived from the ocean surface net heat flux from historical simulations of 35 models archived under the IPCC-AR5/CMIP5  (http://cmip-pcmdi.llnl.gov/cmip5/) is contrasted against the observation-based transports. The historical simulations were derived from models forced by observed atmospheric composition changes reflecting both the anthropogenic (greenhouse gases and aerosols) and natural sources (volcanic, solar forcing, aerosols and emissions of short-lived species and their precursors). The simulations cover much of the industrial period from the mid-nineteenth century to the present and are sometimes referred to as “twentieth century all forcings” simulations. In our analysis we make use of a 15-year climatology based on the last 15 years of the simulation period (1991–2005).
Heat Balance Data
Top-of-atmosphere and surface radiative fluxes are from the latest CERES EBAF v2.8 datasets [34, 46]. The shortwave and longwave TOA fluxes are adjusted within their range of uncertainty to be consistent with the global heating rate inferred from in-situ ocean observations and model simulations [45, 46]. The global mean TOA reflected solar irradiance during the period 2000–2010 is 100 Wm−2 with a stated uncertainty in absolute calibration of 2 % (2σ) or 4 Wm−2 . The surface irradiances in the version 2.8 CERES EBAF surface product  are based on the CERES SYN1deg-lite product and radiative transfer calculations with satellite-retrieved surface, cloud, and aerosol properties as input [15, 33, 34, 56]. CERES EBAF surface fluxes have been shown to agree unprecedentedly well with in situ land surface and ocean observations [22, 34].
With the assumption that both the annual heat storage and transport are negligible over land and within the cryosphere , the hemispheric surface net heat flux (F s) is obtained as the residual of the hemispheric OHS and cross-equatorial ocean heat transport (COHT): F s = OHS − COHT. With this novel approach, we constrain the hemispheric F s by the observed hemispheric OHS. This requires an estimate of the net amount of heat that is moved from one hemisphere to the other. We estimate the COHT indirectly by integrating F s over the area of the ocean (“Cross Equatorial Ocean Heat transport” section) from pole to equator (e.g., ) as opposed to applying the direct method of integrating ocean temperatures and current velocities . This may imply a circular argument in the context of deriving the hemispheric F s, but allows us to exploit a large number of datasets (“Cross Equatorial Ocean Heat transport” section), providing first-order estimates that compare well to previous estimates (see below). An improved representation of the COHT considering ocean dynamics is proposed for a future study.
The surface turbulent fluxes are inferred as a residual of the surface radiative budget (CERES EBAF) and the surface net heat flux derived from the ocean data. This approach to estimate the hemispheric energy budget and the terms that define it differs from the recent analyses of Loeb et al.  and Liu et al. . These latter studies combine the total and atmospheric heat budgets derived from satellite-retrieved TOA irradiances (CERES) and atmospheric reanalysis (ERA-Interim) to infer the surface heat budget as their residual.
Here, the atmospheric heat budget is the residual of the total (derived from CERES TOA fluxes) and the surface heat budget. The total and atmospheric heat transports across the equator correspond to the halved difference between the hemispheric energy budgets and are converted to PW considering a hemispheric surface area of 2.55 × 1014 m2.
Cloud and Precipitation Data
Analysis of cloud and precipitation data derived from the CloudSat spaceborne cloud-profiling radar (CPR, ) also provide context for the discussion of the hemispheric properties of precipitation. The data used are the 2B-GEOPROF-lidar profile data that provide our most definitive measure of cloud top heights and cloud area fractions  and the occurrence of precipitation based on the most recent version of 2C-COLUMN-PRECIP product that determines the incidence of precipitation from estimates of the path-integrated attenuation (PIA) of the CPR. This version has been extended to include precipitation over land (e.g., ). Other precipitation data include the version 2.2 Global Precipitation Climatology Project (GPCP) [1, 26], ERA-interim [14, 57], The Modern Era Retrospective-Analysis for Research and Applications (MERRA) [11, 53], and the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 (NCEP-DOE R2) product . GPCP is a merged product using data from gauges over land, and from spaceborne sensors over both land and ocean including Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), and geostationary and polar orbiting infrared imagers and sounders. The latest version (V2.2) of the monthly 2.5° × 2.5° resolution is used in this study. ERA-interim relies on a 4D-VAR (rather than 3D-Var as in ERA-40) system which uses observations within 12-h windows to initialize forecast simulations. This study uses daily precipitation maps at 1.5° × 1.5° spatial resolution. MERRA uses the Goddard Earth Observing System Data Assimilation System version 5 (GEOS-5; ) to assimilate observations (e.g., radiance data) for the retrospective analyses. In the present study, precipitation is obtained from the latest version of the MERRA product (V 5.2) with resolution of 2/3° longitude by 1/2° latitude. NCEP-DOE R2 uses a global data assimilation system and numerical weather prediction model to produce atmospheric analyses using historical data and also to produce analyses of the current atmospheric state. The spatial and temporal resolutions of the product are ∼1.875° × 1.875° and four times daily, respectively.
Ocean Heat Content and Transports
The Hemispheric Energy Balance
Surface turbulent fluxes are deduced as a residual of the surface heat budget (OHS-COHT), and the CERES EBAF surface radiative fluxes. The values of the hemispheric fluxes and transports taken from Loeb et al.  are also provided for comparison and given in parentheses. Although a different period is considered (2004–2014 vs. 2001–2012 in ), the net heat fluxes at TOA are essentially the same. However, in contrast to Loeb et al.  who assume hemispheric symmetry in OHS, we implicitly take into account that heat entering the oceans (F s) is either stored (OHS) or exchanged between the hemispheres (COHT). The COHT of 0.45 PW corresponds to 1.8 Wm−2 being moved from the SH to the NH. With the OHS of 0.9 Wm−2 (SH) and 0.1 Wm−2 (NH), the respective values of F s are 2.7 Wm−2 (SH) and −1.7 Wm−2 (NH). The uncertainties to be attached to these transports are large (Fig. 4) and it is not possible to provide a precise closure to the heat balance without some adjustment to individual fluxes.
The total heat transport derived from the hemispheric contrast in CERES EBAF TOA net fluxes is 0.23 PW in contrast to the value of 0.12 PW obtained as to the sum of the oceanic and atmospheric heat transport derived from the hemispheric contrast in the atmospheric heat budget. Likewise, the atmospheric heat transport is at 0.22 PW, instead of 0.33 PW, if we assume the value of 0.23 PW from CERES at TOA and the COHT of 0.45 PW. These differences in the cross equatorial heat transports are indicative of the uncertainties in estimating the overall closure and within the uncertainty estimate at 0.6 PW (from ). While the estimates presented in Fig. 4 leave room for discussion and improvement, they do draw a similar picture to those of Loeb et al. .
A notable feature of the energy balance portrayed in Fig. 4 is the near hemispheric symmetry in the TOA energy balance in contrast to the surface radiative fluxes that are slightly more asymmetrical. At the TOA, the SH is slightly out of balance as a result of a small amount of heat being absorbed by the Southern oceans (Fig. 1) whereas the NH appears to be almost in balance. This small hemispheric difference results from a small asymmetry in the OLR (e.g., ) as both the incoming solar and reflected solar are almost identical in both hemispheres (e.g., [72, 65]). The slight energy imbalance between the hemispheres implies a net transport of heat across the equator from the SH to the NH, which composes of a northward heat transport by the oceans and a southward heat transport by the atmosphere. That the NH atmosphere is warmer in the annual mean than the SH (e.g., ) is reflected in the difference in surface longwave flux with the NH emitting almost 7 Wm−2 more radiation into the atmosphere from the warmer NH surface. The hemispheric energy contrast in turn forces an atmospheric transport of heat of 0.33 PW from the NH to the SH (or 0.24 PW according to ). In order to facilitate such a heat transport away from the warmer NH atmosphere to the SH, it has been shown that a displacement of the Hadley circulation is required [19, 78]. This displacement appears to be driven by the northward ocean heat and moisture transports, shifting the precipitation maxima to the NH (; Frierson et al. ). The resulting precipitation asymmetry between hemispheres is quantified in the following section.
The Hemispheric Character of the Hydrological Cycle
Symmetry in Earth System Models (ESMs)
Given the observed hemispheric energy imbalance, cross-equatorial energy transport, and precipitation asymmetry, several studies have sought to investigate the relationship between these features in climate models. Biases in albedo and TOA energy budgets, with related biases in cross-equatorial energy transport, have been associated with the double ITCZ bias observed in many current climate models (e.g., ). Stephens et al.  note that the observed albedo symmetry is not reproduced in the suite of global Earth system models (ESMs) that contributed to the historical CMIP5 simulations. Indeed, a long-standing, large, and ubiquitous radiation bias in climate models is excessive absorption of shortwave radiation (i.e., albedo too low) over the Southern Ocean [28, 69]. Although the potential impact of this lack of hemispheric albedo symmetry has been explored in idealized aqua planet (e.g., ) and slab ocean models (e.g., [13, 19]), recent experiments with Earth system models reveal a very different response when the models possess a fully coupled, dynamical ocean [24, 36]. Indeed, symmetry experiments performed recently using ESMs have yielded surprising results and the results from two classes of recent experiments that used different approaches to force symmetry are reviewed here.
Despite the differing motivations for the experiments, different experimental set-ups and the use of two independently developed models, both studies arrived at a similar conclusion. Imposing hemispheric albedo symmetry to more closely match observations impacts the large-scale circulation, large-scale heat transport, but not tropical precipitation. In both the Kay et al.  and Hawcroft et al.  studies, the experiments produce tropical precipitation that is largely unaffected by imposing hemispheric energy symmetry because the resulting cross-equatorial heat transport changes occurred in the ocean not the atmosphere. These two studies can be contrasted with the improvements in tropical precipitation seen in Haywood et al. . The relationship between cross-equatorial atmospheric heat transport and tropical precipitation asymmetry noted in previous studies [e.g., 13, 73] is reproduced in Haywood et al., though the changes in cross-equatorial atmospheric energy transport can be associated with the local discontinuity in TOA net flux which is induced by the experimental design, rather than a significant improvement in the model's overall performance. Together, these three studies imply that zonal mean tropical precipitation biases in models may be associated with errors in cross-equatorial atmospheric energy transport, that are not resolved by correcting remote energy budget biases, such as those in the Southern Ocean.
Discussion and Conclusions
This paper describes an assembly of a large amount of data used to construct the Earth’s hemispheric energy balance. The data include multiple sources of in situ oceanic temperatures to deduce the oceanic heat content, oceanic analyses that provide ocean transports, and surface and TOA radiative flux data from CERES. Because energy and water are synonymous within the Earth system, the characteristics of the hemispheric precipitation are also reviewed. The main result of the study is summarized in Fig. 4 showing a multi-year annual average of the hemispheric energy balance and the fluxes of heat across the equator. The results of Loeb et al.  are also included and no attempt was made to adjust the fluxes and provide overall balance choosing to underscore the degree to which the different data sources are imbalanced. A notable feature of the energy balance portrayed is the near hemispheric symmetry in the TOA energy balance as has been noted in other studies in contrast to the surface radiative fluxes that are slightly more asymmetrical. At the TOA, the SH is slightly out of balance and a small amount of heat (0.9 Wm−2) is absorbed by the Southern oceans (Fig. 1) whereas the NH appears to be almost in balance. The slight energy imbalance between the hemispheres implies a net transport of heat across the equator from the warmed SH to the NH (Fig. 2) which is realized as a 0.45 PW transport by the oceans and a smaller southward transport of heat (0.22–0.33 PW) by the atmosphere. The turbulent fluxes, deduced in this study from the residual of the net balance at the surface and the surface radiative fluxes, are larger in the SH by about 3 Wm−2. The hemispheric precipitation is also balanced to approximately 3 Wm−2 according to GPCP data with slightly more precipitation accumulating (4 %) in the NH. Precipitation in each hemisphere has very different properties according to CloudSat data that indicates a higher frequency of precipitation in the SH implying more intense precipitation in the NH.
The relationship between the hemispheric energy balance and precipitation in climate models has previously been tendered as an important control on model precipitation biases. The recent studies discussed here [24, 36], which employed two independently developed fully coupled climate models, indicate that simple improvements to the zonal mean energy balance, which lead to changes in total (ocean plus atmosphere) meridional energy transport, do not yield improvements to tropical precipitation biases, as implied by previous studies using aquaplanet or slab ocean configurations. It appears that the precipitation biases are associated with model problems in representing the cross-equatorial heat transport by the atmosphere (e.g., Figure 9) whereas the adjustments to hemispheric equilibration of the TOA budget appear to occur mostly in the oceans. Reducing the underlying physical biases in climate models which cause these biases appears to be a necessary and a challenging hurdle to improve both the energy and precipitation budgets in global models.
Many different sources of data were used in this study. We gratefully acknowledge the help of Seiji Kato for access to the latest version of CERES EBAF data. We thank Doris Folini, Urs Beyerle, and Thierry Corti for their efforts in post-processing and downloading the CMIP5 data. Furthermore, we thank Veronica Nieves, Dimitris Menemenlis, Carmen Boening, and Tong Lee for providing the ocean datasets and useful discussions. Andy Jones is thanked for providing additional analyses. This work was supported under NASA Grants NNN13D984T and NNN12AA01C. MH and JMH were supported by the Natural Environment Research Council/Department for International Development via the Future Climates for Africa (FCFA) funded project Improving Model Processes for African Climate (IMPALA, NE/M017265/1). JMH was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Program (GA01101). JEK was supported by start-up funds awarded to her by the University of Colorado and the Cooperative Institute for Research in Environmental Sciences (CIRES).
- 1.Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, 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 Hydrometeorol. 2003;4(6):1147–67.CrossRefGoogle Scholar
- 4.Antonov JI, Levitus S, Boyer TP. Climatological annual cycle of ocean heat content. Geophys Res Lett. 2004;31(4).Google Scholar
- 7.Behringer, D. W.. The global ocean data assimilation system (GODAS) at NCEP, paper presented at 11th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS). Am. Meteorol. Soc., San Antonio, Tex. 2007.Google Scholar
- 8.Berry DI, Kent EC. Air-sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates. Int J Climatol. 2011;31(7):987–1001. doi: 10.1002/joc.2059.
- 9.Bindoff NL, Willebrand J, Artale V, Cazenave A, Gregory J, Gulev S, Hanawa K, Le Quéré C, Levitus S, Nojiri Y, Shum CK, Talley LD, Unnikrishnan A. Observations: oceanic climate change and sea level. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, editors. In: climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2007.Google Scholar
- 18.Forget, G. A. E. L., Campin, J. M., Heimbach, P., Hill, C. N., Ponte, R. M., & Wunsch, C.. ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. 2015.Google Scholar
- 23.Hakuba, MZ, Folini, D., & Wild, M. On the zonal near constancy of fractional solar absorption in the atmosphere. J Clim. 2016. doi: 10.1175/JCLI-D-15-0277.1.
- 27.Hurrell, J., Holland, M. M., Gent, P. R , Ghan, S., Kay, J. E., Kushner, P., Lamarque, J-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M., Mahowald, N., Marsh, D., Neale, R., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J. and S. Marshall. The Community Earth System Model: A Framework for Collaborative Research. Bull Am Meteor. 2013. Google Scholar
- 32.Kang SM, Seager R, Frierson DMW, X. L. Croll revisited: why is the northern hemisphere warmer than the southern hemisphere? Clim Dyn. 2014;44(5):1457–72.Google Scholar
- 33.Kato, S., Rose, F. G., Sun‐Mack, S., Miller, W. F., Chen, Y., Rutan, D. A., ... & Winker, D. M. Improvements of top‐of‐atmosphere and surface irradiance computations with CALIPSO‐, CloudSat‐, and MODIS‐derived cloud and aerosol properties. J Geophys Res: Atmos. 2011;116(D19).Google Scholar
- 42.Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E., Locarnini, R. A., ... & Zweng, M. M. (2012). World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys Res Lett. 39(10).Google Scholar
- 47.Loeb, N. G., Wang, H., Cheng, A., Kato, S., Fasullo, J. T., Xu, K. M., & Allan, R. P.. Observational constraints on atmospheric and oceanic cross-equatorial heat transports: revisiting the precipitation asymmetry problem in climate models. Clim Dyn. 2015;1–19.Google Scholar
- 48.Mace, G. G., Zhang, Q., Vaughan, M., Marchand, R., Stephens, G., Trepte, C., & Winker, D. (2009). A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data. J Geophys Res: Atmos, 114(D8).Google Scholar
- 52.Peixoto JP, Oort AH. Physics of climate. New York, NY: American Institute of Physics; 1992.Google Scholar
- 54.Roemmich, D., Church, J., Gilson, J., Monselesan, D., Sutton, P., & Wijffels, S. (2015). Unabated planetary warming and its ocean structure since 2006. Nat Clim ChangGoogle Scholar
- 56.Rutan, D., Rose, F., Roman, M., Manalo‐Smith, N., Schaaf, C., & Charlock, T.. Development and assessment of broadband surface albedo from Clouds and the Earth’s Radiant Energy System Clouds and Radiation Swath data product. J Geophys Res: Atmos. 2009;114(D8).Google Scholar
- 59.Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M., ... & L’Ecuyer, T.. CloudSat mission: Performance and early science after the first year of operation. J Geophys Res: Atmos. 2008;113(D8).Google Scholar
- 61.Stephens GL, L'Ecuyer T, Forbes R, Gettlemen A, Golaz J-C, Bodas-Salcedo A, Suzuki K, Gabriel P, Haynes J. Dreary state of precipitation in global models. J Geophys Res. 2010;115:D24211. doi: 10.1029/2010JD014532.
- 65.Stephens GL, O’Brien D, Webster PJ, Pilewski P, Kato S, Li J. The albedo of Earth. Rev Geophys. 2015;53:141–63. doi: 10.1002/2014RG000449.
- 71.Valdivieso, M., Haines, K., Balmaseda, M., Chang, Y. S., Drevillon, M., Ferry, N., ... & Wang, X.. An assessment of air–sea heat fluxes from ocean and coupled reanalyses. Clim Dyn. 2015;1–26.Google Scholar
- 75.von Schuckmann KV, Le Traon PYL. How well can we derive Global Ocean indicators from Argo data? Ocean Sci. 2011;7(6):783–91.Google Scholar
- 79.Yu, L., Jin, X., & Weller, R. A. (2008). Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. OAFlux Project Technical Report OA-2008-01, 64 pp.Google Scholar