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Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection

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Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection 1JUNE 2017 L I U E T A L . 3979 Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection a b a b b c XU LIU, WAN WU, BRUCE A. WIELICKI, QIGUANG YANG, SUSAN H. KIZER, XIANGLEI HUANG, c a a a XIUHONG CHEN, SEIJI KATO, YOLANDA L. SHEA, AND MARTIN G. MLYNCZAK a NASA Langley Research Center, Hampton, Virginia b Science Systems and Applications, Inc., Hampton, Virginia c Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan (Manuscript received 28 September 2016, in final form 8 February 2017) ABSTRACT Detecting climate trends of atmospheric temperature, moisture, cloud, and surface temperature requires accurately calibrated satellite instruments such as the Climate Absolute Radiance and Refractivity Ob- servatory (CLARREO). Previous studies have evaluated the CLARREO measurement requirements for achieving climate change accuracy goals in orbit. The present study further quantifies the spectrally de- pendent IR instrument calibration requirement for detecting trends of atmospheric temperature and moisture profiles. The temperature, water vapor, and surface skin temperature variability and the associ- ated correlation time are derived using the Modern-Era Retrospective Analysis for Research and Appli- cations (MERRA) and European Centre 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 conditions. A 0.04-K (k 5 2; 95% confidence) radiometric calibration requirement baseline is derived using a spectral fingerprinting method. It is also demonstrated that the requirement is spectrally dependent and that some spectral regions can be relaxed as a result of the hyperspectral nature of the CLARREO instrument. Relaxing the requirement to 0.06 K (k 5 2) is discussed further based on the uncertainties associated with the temperature and water vapor natural variability and relatively small delay in the 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. Introduction calibrate other satellite instruments so that data such as those from operational weather sounders and from the The CLARREO mission has been proposed to pro- Earth energy budget instruments can be used to improve vide the essential observations for climate change on climate change detection. decadal time scales with high accuracy that are traceable To detect an accurate trend for a geophysical pa- to International System of Units (SI) standards. The rameter, the observation system has to be able to sepa- demand for high absolute calibration accuracy of the rate the natural variability from anthropogenic climate Climate Absolute Radiance and Refractivity Observa- changes. Therefore, even for a perfect observation sys- tory (CLARREO) instrument is driven by the need to tem, one has to make sufficiently long observations to accurately determine the climate trend with minimum minimize the contribution from the natural variability. time delay relative to a perfect observation system For a perfect observation system, the trend uncertainty (Wielicki et al. 2013) and by the need to accurately for a selected geophysical parameter is statistically de- termined by its variability svar and autocorrelation time tvar as has been explained by both Weatherhead et al. Denotes content that is immediately available upon publica- (1998) and Leroy et al. (2008a). How the measurement tion as open access. uncertainty affects the trend detection uncertainty is quantified by the accuracy uncertainty factor Ua Corresponding author e-mail: Xu Liu, [email protected] (Wielicki et al. 2013), where Ua is given as DOI: 10.1175/JCLI-D-16-0704.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/06/21 02:30 PM UTC 3980 JOURNAL OF CLIMATE VOLUME 30 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 21 s2 t 1 s2 t 1 s2 t spectral coverage of the far IR from 200 to 645 cm , U 5 1 1 cal cal instru instru orbit orbit . (1) a s2 t which is not currently included in hyperspectral sounders var var such as the Cross-Track Infrared Sounder (CrIS), the Atmospheric Infrared Sounder (AIRS), and the Infrared The variable U defines the ratio of the trend detection a Atmospheric Sounding Interferometer (IASI), will allow uncertainty of a real system over that of a perfect system. the CLARREO instrument to measure nearly half of the The measurement uncertainty includes the calibration outgoing longwave radiation currently unobserved by s , instrument noise s , and orbit sampling error cal instru current sounders and will provide additional information s uncertainties, with their associated autocorrela- orbit on cirrus clouds and upper-tropospheric water vapor. The tion times t , t , and t . We can derive the cali- cal instru orbit CO atmospheric emission lines with various trans- bration requirement as follows: 2 mittances will provide vertical temperature profile in- sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi formation. The H2O emission lines will provide water (U2 2 1)s2 t 2 s2 t 2 s2 t s 5 a var var instru instru orbit orbit (2) vapor vertical profile information. The window spectral cal t cal regions will provide information on surface skin tem- perature and surface emissivity. The broad spectral cov- In this paper, we assume that calibration uncertainty is the erage will enable the CLARREO instrument to dominant factor of the total measurement uncertainty. characterize cloud-top height, cloud phase, cloud Other factors such as the uncertainty due to instrument amount, and cloud particle size. random noise can be minimized by performing spatial and Because of the hyperspectral nature of the IR in- temporal averaging of the observed spectra. Wielicki et al. strument, information from one channel may be highly (2013) have concluded that the orbital sampling error is correlated with others. For example, the CO2 n2 perpen- small compared to natural variability even with just one dicular vibrational band near 15 mm has P, Q, and R 8 90 orbit. Equation (2) can be further simplified as branches. The R branch, which is located on the shorter- sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi wavelength side of the Q branch, has similar information (U2 2 1)t content as the P branch, which is on the longer-wavelength s 5 a var s , (3) cal t var cal side of the Q branch. We can tolerate larger calibration errors for those channels in the CO2 P branch as long as we where scal is the observation accuracy for a geophysical can accurately calibrate the spectral region that covers the parameter that can be achieved assuming some value for R branch (or vice versa). Based on this rationale, we may the trend detection uncertainty factor Ua. It should be be able to relax the calibration requirement for spectral noted here that the calibration requirement scal defined in regions where the transmittances of the Fourier transform Eqs. (2) and (3) is not the direct spectral calibration re- spectrometer (FTS) optics or the detector sensitivities are quirement imposed on the instrument. It is the observa- low (e.g., at spectral band edges). tion accuracy uncertainty of geophysical parameters that The details of this study are contained in sections 2 are essential to climate change study. To obtain the and 3. Section 2 of this paper describes the efforts to derive spectral calibration requirement for the Fourier- natural variability values using deseasonalized MERRA transform-based IR instrument of CLARREO, the in- (Rienecker et al. 2011) and ECMWF interim reanalysis verse relationship between the spectral calibration error (ERA-Interim; Dee et al. 2011) data, which include and the associated error for the geophysical variables the information from multiple decades of satellite data. needs to be established. The attribution of the change in Our approach follows the trend analysis methodology of the measured IR spectra to climate change signals (i.e., Weatherhead et al. (1998) and Leroy et al. (2008a).Both changes in temperature, water vapor, cloud property, and methods assume the representation of climate anomalies surface property) has been studied using spectral finger- in a time series using a linear trend model with noise printing methods (Leroy et al. 2008b; Huang et al. 2010; processes (natural variability) embedded and correlated Kato et al. 2011). We use a similar method to perform the amongsuccessivemeasurements. Climate anomalies here inversion of radiance change to the geophysical parameter can be viewed as a linear combination of the climate change. Our goal is to characterize a spectrally dependent trends [a0 in Eq. (4)], the climate variations associated with instrument calibration requirement so that we can accu- known climate forcing factors, and the natural variability: rately detect the atmospheric temperature and moisture Y(t) 5 a t 1 C(t) 1 «, (4) profile changes within the uncertainties defined by scal. 0 The nominal design of the IR spectrometer of 2 CLARREO has a 0.5-cm 1 spectral resolution with a where Y is the climate anomalies as a function of time t, 2 spectral coverage from 200 to 2000 cm 1. The additional C is the contribution of climate forcing factors, and « is Unauthenticated | Downloaded 10/06/21 02:30 PM UTC 1JUNE 2017 L I U E T A L . 3981 the natural variability. The effects of major climate forcing factors, including volcanic eruptions, solar cycle forcing, El Niño–Southern Oscillation (ENSO) vari- ability, and the quasi-biennial oscillation (QBO), in the time series data have been accounted for in our linear regression analysis. Although ENSO and QBO are classified as ‘‘internal’’ forcing factors, the success of including them in the climate model simulations (Philander et al.
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