NATURE GEOSCIENCE | VOL 5 | OCTOBER 2012 | www.nature.com/naturegeoscience 691 E arth's climate is determined by the flows of energy into and out of the planet and to and from Earth's surface. Geographical distributions of these energy flows at the surface are particularly important as they drive ocean circulations, fuel the evaporation of water from Earth's surface and govern the planetary hydrological cycle. Changes to the surface energy balance also ultimately control how this hydrological cycle responds to the small energy imbalances that force climate change 1 .The seminal importance of Earth's energy balance to climate has been understood for more than a century. Although the earliest depictions of the global annual mean energy budget of Earth date to the beginning of the twentieth century 2,3 , the most significant advance to our understanding of this energy balance occurred after the space age in the 1960s. Among the highlights obtained from early satellite views of Earth was the measurement of Earth's albedo (the ratio of outgoing flux of solar energy to incoming flux from the Sun) at approximately 30% (ref. 4), thus settling a long-standing debate on its magnitude -values ranged between 89% and 29% (ref. 5) before these measurements. The sign and magnitude of the net effect of clouds on the top-of-atmosphere (TOA) fluxes 6 was also later established with the space-borne observations of the scanning instrument on the Earth Radiation Budget Experiment (ERBE) 7 , which better delineated between clear and cloudy skies. ERBE, and later the Clouds and the Earth's Radiant Energy System (CERES) 8 and the French Scanner for Radiation Budget 9 , confirmed that the global cloud albedo effect was significantly larger than the greenhouse effect of clouds. Although this was a major advance at the time, determining the influence of clouds on atmospheric and surface fluxes had to wait until the recent satellite measurements of the vertical structure of clouds became available from the A-train 10 .Climate change is governed by changes to the global energy balance. At the top of the atmosphere, this balance is monitored globally by satellite sensors that provide measurements of energy flowing to and from Earth. By contrast, observations at the surface are limited mostly to land areas. As a result, the global balance of energy fluxes within the atmosphere or at Earth's surface cannot be derived directly from measured fluxes, and is therefore uncertain. This lack of precise knowledge of surface energy fluxes profoundly affects our ability to understand how Earth's climate responds to increasing concentrations of greenhouse gases. In light of compilations of up-to-date surface and satellite data, the surface energy balance needs to be revised. Specifically, the longwave radiation received at the surface is estimated to be significantly larger, by between 10 and 17 Wm -2 , than earlier model-based estimates. Moreover, the latest satellite observations of global precipitation indicate that more precipitation is generated than...

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[1] One year of instantaneous top-of-atmosphere (TOA) and surface shortwave and longwave irradiances are computed using cloud and aerosol properties derived from instruments on the A-Train Constellation: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, the CloudSat Cloud Profiling Radar (CPR), and the Aqua Moderate Resolution Imaging Spectrometer (MODIS). When modeled irradiances are compared with those computed with cloud properties derived from MODIS radiances by a Clouds and the Earth's Radiant Energy System (CERES) cloud algorithm, the global and annual mean of modeled instantaneous TOA irradiances decreases by 12.5 W m −2 (5.0%) for reflected shortwave and 2.5 W m −2 (1.1%) for longwave irradiances. As a result, the global annual mean of instantaneous TOA irradiances agrees better with CERES-derived irradiances to within 0.5W m −2 (out of 237.8 W m −2 ) for reflected shortwave and 2.6W m −2 (out of 240.1 W m −2 ) for longwave irradiances. In addition, the global annual mean of instantaneous surface downward longwave irradiances increases by 3.6 W m −2 (1.0%) when CALIOP-and CPR-derived cloud properties are used. The global annual mean of instantaneous surface downward shortwave irradiances also increases by 8.6 W m −2 (1.6%), indicating that the net surface irradiance increases when CALIOP-and CPR-derived cloud properties are used. Increasing the surface downward longwave irradiance is caused by larger cloud fractions (the global annual mean by 0.11, 0.04 excluding clouds with optical thickness less than 0.3) and lower cloud base heights (the global annual mean by 1.6 km). The increase of the surface downward longwave irradiance in the Arctic exceeds 10 W m −2 (∼4%) in winter because CALIOP and CPR detect more clouds in comparison with the cloud detection by the CERES cloud algorithm during polar night. The global annual mean surface downward longwave irradiance of 345.4 W m −2 is estimated by combining the modeled instantaneous surface longwave irradiance computed with CALIOP and CPR cloud profiles with the global annual mean longwave irradiance from the CERES product (AVG), which includes the diurnal variation of the irradiance. The estimated bias error is −1.5 W m −2 and the uncertainty is 6.9 W m −2 . The uncertainty is predominately caused by the near-surface temperature and column water vapor amount uncertainties.Citation: Kato, S., et al. (2011), Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties,

[1] In the biomass, soils, and peatlands of Siberia, boreal Russia holds one of the largest pools of terrestrial carbon. Because Siberia is located where some of the largest temperature increases are expected to occur under current climate change scenarios, stored carbon has the potential to be released with associated changes in fire regimes. Our concentration is on estimating a wide range of current and potential emissions from Siberia on the basis of three modeled scenarios. An area burned product of Siberia is introduced, which spans from 1998 through 2002. Emissions models are spatially explicit; therefore area burned is extracted from associated ecoregions for each year. Carbon consumption estimates are presented for 23 unique ecoregions across Siberia, which range from 3.4 to 75.4 t C ha À1 for three classes of severity. Total direct carbon emissions range from the traditional scenario estimate of 116 Tg C in 1999 (6.9 M ha burned) to the extreme scenario estimate of 520 Tg C in 2002 (11.2 M ha burned), which are equivalent to 5 and 20%, respectively, of total global carbon emissions from forest and grassland burning. Our results suggest that disparities in the amount of carbon stored in unique ecosystems and the severity of fire events can affect total direct carbon emissions by as much as 50%. Additionally, in extreme fire years, total direct carbon emissions can be 37-41% greater than in normal fire years, owing to increased soil organic matter consumption. Mean standard scenario estimates of CO 2 (555-1031 Tg), CO (43-80 Tg), CH 4 (2.4-4.5 Tg), TNMHC (2.2-4.1 Tg), and carbonaceous aerosols (4.6-8.6 Tg) represent 10, 15, 19, 12 and 26%, respectively, of the global estimates from forest and grassland burning. Accounting for smoldering combustion in soils and peatlands results in increases in CO, CH 4 , and TNMHC and decreases in CO 2 emitted from fire events.

[1] Among the largest uncertainties in quantifying the radiative impacts of clouds are those that arise from the inherent difficulty in precisely specifying the vertical distribution of cloud optical properties using passive satellite measurements. Motivated by the need to address this problem, CloudSat was launched in April 2006 carrying into orbit the first millimeter wavelength cloud radar to be flown in space. Retrieved profiles of liquid and ice cloud microphysical properties from this Cloud Profiling Radar form the basis of the CloudSat's fluxes and heating rates algorithm, 2B-FLXHR, a standard product that provides high vertical resolution profiles of radiative fluxes and atmospheric heating rates on the global scale. This paper describes the physical basis of the 2B-FLXHR algorithm and documents the first year of 2B-FLXHR data in the context of assessing the radiative impact of clouds on global and regional scales. The analysis confirms that cloud contributions to atmospheric radiative heating are small on the global scale because of a cancelation of the much larger regional heating from high clouds in the tropics and cooling from low clouds at higher latitudes. Preliminary efforts to assess the accuracy of the 2B-FLXHR product using coincident CERES data demonstrate that outgoing longwave fluxes are better represented than those in the shortwave but both exhibit good agreement with CERES on scales longer than 5 days and larger than 5°. Colocated CALIPSO observations of clouds that are undetected by CloudSat further indicate that while thin cirrus can introduce modest uncertainty in the products, low clouds that are obscured by ground clutter represent a far more important source of error in the current 2B-FLXHR product that must be addressed in subsequent versions of the algorithm.

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Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m −2 and a bias of less than 5 W m −2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500-1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1-3 measurement platforms in each nominal 10 • by 10 • box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development,