Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection.

Detecting climate trends of atmospheric temperature, moisture, cloud, and surface temperature requires accurately calibrated satellite instruments such as the Climate Absolute Radiance and Reflectivity Observatory (CLARREO). Wielicki et al. have studied the CLARREO measurement requirements for achieving climate change accuracy goals in orbit. Our study further quantifies the spectrally dependent IR instrument calibration requirement for detecting trends of atmospheric temperature and moisture profiles. The temperature, water vapor, and surface skin temperature variability and the associated correlation time are derived using Modern Era Retrospective-Analysis for Research and Applications (MERRA) and European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results are further validated using climate model simulation results. With the derived natural variability as the reference, the calibration requirement is established by carrying out a simulation study for CLARREO observations of various atmospheric states under all-sky. We derive a 0.04 K (k=2, or 95% confidence) radiometric calibration requirement baseline using a spectral fingerprinting method. We also demonstrate that the requirement is spectrally dependent and some spectral regions can be relaxed due to the hyperspectral nature of the CLARREO instrument. We further discuss relaxing the requirement to 0.06 K (k=2) based on the uncertainties associated with the temperature and water vapor natural variability and relatively small delay in time-to-detect for trends relative to the baseline case. The methodology used in this study can be extended to other parameters (such as clouds and CO2) and other instrument configurations.

[1]  Veronika Eyring,et al.  A Summary of the CMIP5 Experiment Design , 2010 .

[2]  M. Iacono,et al.  Line‐by‐line calculation of atmospheric fluxes and cooling rates: 2. Application to carbon dioxide, ozone, methane, nitrous oxide and the halocarbons , 1995 .

[3]  K. Wolter,et al.  El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext) , 2011 .

[4]  Yi Huang,et al.  Separation of longwave climate feedbacks from spectral observations , 2010 .

[5]  M. Mlynczak,et al.  A Comparison of Climate Signal Trend Detection Uncertainty Analysis Methods , 2014 .

[6]  Stephen S. Leroy,et al.  Climate Signal Detection Times and Constraints on Climate Benchmark Accuracy Requirements , 2008 .

[7]  M. Takahashi Simulation of the Quasi‐Biennial Oscillation in a general circulation model , 1999 .

[8]  L. Gray,et al.  Characterization of the 11-Year Solar Signal Using a Multiple Regression Analysis of the ERA-40 Dataset , 2005 .

[9]  D. Marsh,et al.  On the detection of the solar signal in the tropical stratosphere , 2013 .

[10]  Hugh L. Dryden,et al.  THE NATIONAL AERONAUTICS AND SPACE ADMINISTRATION , 1958 .

[11]  David Rind,et al.  How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006 , 2008 .

[12]  A. Powell,et al.  Evaluation of the Temperature Trend and Climate Forcing in the Pre- and Post Periods of Satellite Data Assimilation , 2013 .

[13]  Ashish Sharma,et al.  Evaluation of volcanic aerosol impacts on atmospheric water vapor using CMIP3 and CMIP5 simulations , 2013 .

[14]  Nipa Phojanamongkolkij,et al.  Detection of Atmospheric Changes in Spatially and Temporally Averaged Infrared Spectra Observed from Space , 2011 .

[15]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[16]  William L. Smith,et al.  Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels , 2009 .

[17]  M. Iacono,et al.  Line-by-Line Calculations of Atmospheric Fluxes and Cooling Rates: Application to Water Vapor , 1992 .

[18]  J. Angell,et al.  Tropospheric temperature variations adjusted for El Niño, 1958–1998 , 2000 .

[19]  Andrew E. Dessler,et al.  Trends in tropospheric humidity from reanalysis systems , 2010 .

[20]  B. Santer,et al.  Accounting for the effects of volcanoes and ENSO in comparisons of modeled and observed temperature trends , 2001 .

[21]  Xu Liu,et al.  Fast and accurate hybrid stream PCRTM-SOLAR radiative transfer model for reflected solar spectrum simulation in the cloudy atmosphere. , 2016, Optics express.

[22]  Xu Liu,et al.  Case‐study of a principal‐component‐based radiative transfer forward model and retrieval algorithm using EAQUATE data , 2007 .

[23]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[24]  Ngar-Cheung Lau,et al.  Simulation of ENSO with a Global Atmospheric GCM Coupled to a High-Resolution, Tropical Pacific Ocean GCM , 1992 .

[25]  Ramaswamy,et al.  The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3 , 2011 .

[26]  Eva Borbas,et al.  Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multispectral Satellite Radiance Measurements , 2008 .

[27]  Denis Tremblay,et al.  Radiometric consistency assessment of hyperspectral infrared sounders , 2015 .

[28]  Nipa Phojanamongkolkij,et al.  Achieving Climate Change Absolute Accuracy in Orbit , 2013 .

[29]  A. Dai Recent climatology, variability, and trends in global surface humidity , 2006 .

[30]  Masaaki Takahashi,et al.  Simulation of the stratospheric Quasi-Biennial Oscillation using a general circulation model , 1996 .

[31]  Daniel K. Zhou,et al.  Development of a fast and accurate PCRTM radiative transfer model in the solar spectral region. , 2016, Applied optics.

[32]  Isabel F. Trigo,et al.  A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms , 2016, Remote. Sens..

[33]  T. Wigley ENSO, volcanoes and record‐breaking temperatures , 2000 .

[34]  John A. Dykema,et al.  Testing Climate Models Using Thermal Infrared Spectra , 2008 .

[35]  Xu Liu,et al.  Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept. , 2006, Applied optics.

[36]  Klaus Hasselmann,et al.  Multi-pattern fingerprint method for detection and attribution of climate change , 1997 .

[37]  Peter W. Thorne,et al.  Attribution of observed surface humidity changes to human influence , 2007, Nature.

[38]  Stefan Rahmstorf,et al.  Global temperature evolution 1979–2010 , 2011 .

[39]  A. J. Miller,et al.  Factors affecting the detection of trends: Statistical considerations and applications to environmental data , 1998 .

[40]  J. Hansen,et al.  Stratospheric aerosol optical depths, 1850–1990 , 1993 .

[41]  W. Paul Menzel,et al.  Global profile training database for satellite regression retrievals with estimates of skin temperature and emissivity , 2005 .