Interannual variability of top-ofatmosphere albedo observed by CERES instruments Seiji Kato NASA Langley Research Center Hampton, VA SORCE Science team meeting, Sedona, Arizona, Sep. 13-16, 2011
TOA irradiance derived from CERES CERES: Clouds and Earth s Radiant Energy System Broadband instruments Measure shortwave, longwave (total-sw), and window radiances in a cross-track mode Currently, CERES instruments are on Terra and Aqua. Terra data start from March 2000 Aqua data start from July 2002 One instrument is on NPP Footprint size of instruments on Terra and Aqua is ~20 km TOA irradiance stability is 0.5 Wm -2 per decade More information is on http://ceres.larc.nasa.gov/
TOA SW Irradiance (W m -2 ) TOA irradiances are derived from angular distribution models Annual mean derived from 4-years of data (from March 2000 Feb. 2004)
TOA LW Irradiance (W m -2 ) Annual mean derived from 4-years of data (from March 2000 Feb. 2004)
Outline of this talk CERES instruments Top-of-atmosphere irradiance derived from CERES radiance measurements Surface irradiance computation De-seasonalized anomalies (defined in later slide) of irradiances Relationship between TOA albedo and cloud fraction de-seasonalized anomalies
Surface irradiances Surface shortwave and longwave irradiances are modeled by a radiative transfer model Inputs Temperature and humidity: Reanalysis Cloud and aerosol properties: MODIS, Geostationary satellites Consistency check Comparison with CERES derived irradiance (TOA) Comparison with surface observations CloudSat, CALIPSO, AIRS
Surface downward and upward irradiance (March 2000) Temperature and humidity: Reanalysis Cloud and aerosol properties: MODIS Modeled irradiances with a 2-stream model
Zonal vertical heating rate profile (200807) Atmospheric absorption = (TOA down TOA up) (Surface down Surface up) Modeled heating rates at Aqua overpass time
Some definitions to analyze variability Deseasonalized anomalies ΔF (e.g. deviation from a monthly mean value) F F F F 1 n Investigate the variability of radiation budget n i 1 F i
Standard deviation of deseasonalized anomalies at TOA (March 2000 to Feb 2004) Standard deviations computed from 1 o X 1 o monthly anomalies
Standard deviation of NET (SW+LW) anomalies (March 2000 to Feb 2004) Thick high clouds: Negative SW anomalies and positive LW anomalies partially cancel Low clouds: LW anomalies are small compared to SW anomalies
Shortwave TOA Flux anomaly (Wm -2 ) TOA albedo anomalies over tropics are well correlated with cloud fraction anomalies MODIS cloud fraction anomaly Loeb et al. GRL 2007 Why are albedo variability and cloud fraction variability so small?
Correlation between TOA shortwave irradiance and cloud cover anomalies Correlation coefficients in polar regions are smaller
Global mean reflected shortwave and cloud fraction anomalies Cloud cover anomalies monthly Reflected Shortwave anomalies 12month average When anomalies are averaged, global mean interannual variability is small
TOA irradiance and cloud cover anomalies The annual and global mean TOA reflected shortwave is 96.7 W m -2, with maximum and minimum value of 97.1 and 96.4 W m -2. The difference between the maximum and minimum values is 0.8% of the mean value. The annual and global mean OLR is 239 W m -2, and the difference between maximum and minimum values is 0.3% of the mean value. The annual and global mean cloud cover is 61.7%, and the difference between maximum and minimum values is 0.2% of the mean value.
Questions Why is global mean interannual variability of albedo so small compared to its mean value? Or what prevents the albedo perturbation by clouds to intensify with time? Investigate the reason from atmospheric energy perspective
Net shortwave irradiance anomalies Tropics 30 N to 30 S From March 2000 Shortwave irradiance anomalies caused by clouds is predominately deposited to the surface
Regional variability Standard deviation of 1 1 anomalies Similar to TOA anomalies, large variability is due to ENSO
Regional variability (Longwave) Standard deviation of 1 1 anomalies Wm -2 Wm -2
Surface SW net versus LW net Correlation between Δ surface SW net and Δ surface LW net F SW net (sfc) c p w h e d T dt F L F s F net LW (sfc) c p w h e v T Energy deposited by shortwave anomalies are used for Surface temperature change Latent heat flux Sensible heat flux Longwave emission Horizontal advection by ocean currents Negative correlation More SW net gives Less LW net Where positive net Is downward net flux
Correlation coefficients between atmospheric net irradiance and cloud cover Atmospheric net irradiance (SW + LW, energy deposited to the atmosphere by radiation) anomalies is well correlated with cloud fraction anomalies, indicating A close connection between cloud fraction variability and atmospheric energy
Assumption Albedo (Insolation) Clouds Outgoing longwave radiation Meridional temperature gradient Large scale dynamics Assumption: Large-scale dynamics, which is driven by temperature gradient, controls global mean cloud cover i.e. If large scale dynamics is constant, cloud cover is also constant
Zonal temperature anomalies Zonal mean thermodynamic equation Separate zonal mean temperature into climatological mean and anomaly Assumptions: Vertical velocity anomalies = C T Horizontal temperature advection anomalies = Diabatic heating anomalies = T t D 2 a T J (t) c p T y 2 (a C) T D 2 y 2 T J' c p T T c T T T 0 e (a C D 2 )t e i y where a < 0, C <0, and D>0 Kato, J Climate, 2009
Atmospheric temperature vs. Cloud cover Correlation coefficient between 300 hpa 1000 hpa geopotential height difference and cloud cover
Cloud cover decorrelation time Month Derived from 48 months of CALIPSO and CloudSat data Decorrelation time = (1+Φ)/(1-Φ), where Φ is autocorrelation with 1 month lag
Summary Interannual variability of global mean albedo is 0.8% of the mean value and interannual variability of longwave is 0.3% of the mean value. A large part of albedo variability is caused by cloud cover variability. Proposed hypothesis Temperature anomalies caused by albedo variability is dumped by dynamics and longwave emission. The time scale of the decay is smaller than a year. As a consequence, temperature anomalies decay exponentially with time, which provides small interannual variability of temperature and cloud cover.
Back-ups
Standard Deviation (W m -2 ) Zonal variability Latitude ( o ) Latitude ( o ) Latitude ( o ) Solid line: Mean standard deviation of 1 X 1 region in a latitudinal zone Dashed line: Standard deviation of zonal anomalies
Cloud cover versus albedo Interannual variability of cloud cover is less than 1% of the mean value Regions with 0.8 or greater correlation coefficient are colored
Zonal atmospheric SW, LW NET and temperature variability Atmospheric SW, LW, and NET 300 hpa 1000 hpa thickness Temporal variability of zonal mean computed with 48 monthly zonal deseasonalized anomalies Temperature variability in polar regions is caused by dynamics variabilities
A large LW net or Upward longwave irradiance anomalies do not appear very often over ocean
Clear-sky, All-sky variability Open circles: Clear-sky Closed circles: All-sky
Correlation between TOA shortwave irradiance and cloud cover anomalies
Cloud fraction