DOCSLIB.ORG

Testing Climate Models and Climate Prediction

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Testing Climate Models and Climate Prediction Testing Climate Models with CLARREO: Feedbacks and Equilibrium Sensitivity Stephen Leroy, John Dykema, Jon Gero, Jim Anderson Harvard University, Cambridge, Massachusetts 21 October 2008 Talk Outline • Feedbacks and Equilibrium Sensitivity • Climate OSSE – Optimal Methods/Multi-pattern regression – Response: GPS Radio Occultation (RO) – Feedbacks: Clear-sky Thermal IR Spectra • Discussion 21 October 2008 CLARREO: Feedbacks and Sensitivity 2 Climate Feedback 21 October 2008 CLARREO: Feedbacks and Sensitivity 3 Climate Feedback (2) Radiative forcing ΔFrad Longwave cooling ΓΔT Amplification or suppression of greenhouse effect, γΔT SW LW SW Δ +Frad Δγ T +γ TT Δ = Γ Δ ∂F dx ∑i ∑i γ SW = i i i i ∂x dT −1 i LW ⎡ SW LW ⎤ ∂F dx Δ =TF Δ Γγ − γ − γ LW = i rad ⎢ ∑i ∑i ⎥ i ⎣ i i ⎦ ∂xi dT 21 October 2008 CLARREO: Feedbacs and Sensitivityk 4 Feedback Uncertainty Bony, S., et al., 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19, 3445-3482. 21 October 2008 CLARREO: Feedbacks and Sensitivity 5 Feedbacks and Climate Prediction Hansen, J. et al., 1985: Climate response times: Dependence on climate sensitivity ean mixing. and ocScience, 229, 857-859. ( 2T CO× ) −T ( 1 × CO ) s ≡ 2 2 ( 2Fradiative CO×2 ) −Fradiative ( 1 × 2 CO ) −1 ⎛ longwave shortwave ⎞ =⎜ Γ∑ −γ i − ∑γ i ⎟ ⎝ i i ⎠ −1 β=s() × C ρ ocean d dT =( Δsβ F = ()βs Δ F T − ) Δ dt imbalance radiative t T t()= T + sβ F Δ ()′ t−β()t e − t′ ′ d t 0 ∫0 rad 21 October 2008 CLARREO: Feedbacs and Sensitivityk 6 Climate OSSE: The Science of a Benchmark Benchmark Climate OSSE Climate Measurement Uncertainty • Traceable to • Simulate trends in • Shortwave forcing observable as international standards • Longwave forcing produced by different • Minimize sampling models • Climate feedbacks & error sensitivity • Explore information content with various contravariant fingerprints Climate OSSE Results • Detection time and accuracy requirements • How measurement constrains climate predictability • Relative redundancy with other benchmark data types 21 October 2008 CLARREO: Feedbacks and Sensitivity 7 Calibration: Double Differencing Hardy, K.R., G.A. Hajj, and E.R. Kursinski, 1994: Accuracies of atmospheric profiles obtained from GPS occultations. Int. J. Sat. Comm., 12, 463-473. 21 October 2008 CLARREO: Feedbacks and Sensitivity 8 Optimal Fingerprinting/Multi-pattern Regression We are limited by the naturally occurring inter-annual variability of the climate system…so optimize. Find signal amplitudes (αm) and uncertainty (Σα) in a data set (d) according to the signals’ patterns (si) against a background of natural variability, the eigenvectors and eigenvalues of which are eμ and λμ. 21 October 2008 CLARREO: Feedbacks and Sensitivity 9 GPS Radio Occultation • Refractivity p p ( 1 ) 10= (N 77 − n . 6 ×6 K = hPa- 1 + ) ( ×3 363 2 - 1 w 10 K hPa ) T T 2 • “Dry” pressure ∞ p()q h dp ( ) ( 4p . h= 402 × 10−4 N- 1 hPa( dh )≅ m ( p ) + 7521 h K ) d ∫ ∫ h 0 T • Geopotential height ⎡ 1 2 2 1 2 2 ⎤ = Φh (()⎢ −r Ωrs)( − Φrs −)/msl⎥ Ω g 0 ⎣ 2 2 ⎦ 21 October 2008 CLARREO: Feedbacs and Sensitivityk 01 GPS RO Dry Pressure Tendency 21 October 2008 CLARREO: Feedbacks and Sensitivity 11 How Does GPS RO Test GCMs? α = global average surface air temperature, d = GPS RO dry pressure [height] Poleward migration of jet 20 2 streams 15 ] -1 1 km 10 -1 Height [km] 0 5 [K ster Increased ITCZ 0 -1 humidity 90 60 30 0 -30 -60 -90 Latitude 2 1 0 Near perfect 2000 2010 2020 2030 2040 2050 Surface Air Temperature [K] Year tracking of global average surface air temperature 21 October 2008 CLARREO: Feedbacks and Sensitivity 12 Thermal Infrared Spectra 21 October 2008 CLARREO: Feedbacks and Sensitivity 13 Thermal Infrared Spectra (2) ] -1 5 decade -1 0 ster -1 ) -1 -5 (cm Tropics, SRES A1B, clear -2 NCAR CCSM3 GFDL CM2.0 W cm -10 MPI/ECHAM5-OPYC -8 UKMO HadCM3 -15 -20 Radiance Trend [10 500 1000 1500 2000 Frequency [cm-1] 21 October 2008 CLARREO: Feedbacks and Sensitivity 14 How Does Spectral IR Test GCMs? Carbon Dioxide Tropospheric Temperature Cumulative Signal 0.0 0.0 -0.2 -0.2 0.0 rad] -6 -0.4 -0.4 500 1000 1500 2000 500 1000 1500 2000 Water Vapor Stratospheric Temperature -0.2 Radiance [10 0.0 0.0 Δ -0.4 -0.2 -0.2 500 1000 1500 2000 Frequency -0.4 -0.4 500 1000 1500 2000 500 1000 1500 2000 21 October 2008 CLARREO: Feedbacks and Sensitivity 15 Applied Scalar Prediction 1.0 0.2 ] Tropospheric Temperature Signal Stratospheric Temperature Signal -1 0.5 0.1 ster -2 0.0 0.0 -0.5 -0.1 Radiance [W m Δ -1.0 -0.2 1.0 0.2 ] Water Vapor Signal Carbon Dioxide Signal -1 0.5 0.1 ster -2 0.0 0.0 -0.5 -0.1 Radiance [W m Δ -1.0 -0.2 0 5 10 15 20 0 5 10 15 20 Time [years] Time [years] 21 October 2008 CLARREO: Feedbacks and Sensitivity 16 Summary • Trends in GPS radio occultation data bear strongly on global average surface air temperature. • Trends in the outgoing longwave spectrum can be used to monitor longwave forcing and constrain all longwave feedbacks observationally. Optimization in space necessary to reduce detection times. • Work in progress includes simulations in cloudy skies and shortwave trends. 21 October 2008 CLARREO: Feedbacks and Sensitivity 17 Backup Slides 21 October 2008 CLARREO: Feedbacks and Sensitivity 18 Applied Scalar Prediction Find signal amplitudes (αm) and uncertainty (Σα) in a data set (d) according to the signals’ patterns (si) against a background of natural variability, the eigenvectors and eigenvalues of which are eμ and λμ. 21 October 2008 CLARREO: Feedbacks and Sensitivity 19 Applied Scalar Prediction Signal #1 Signal #2 Signal #3 Signal #4 Find signal amplitudes (αm) and uncertainty (Σα) in a data set (d) according to the signals’ patterns (si) against a background of natural variability, the eigenvectors and eigenvalues of which are eμ and λμ. 1000 2000 1000 2000 1000 2000 1000 2000 Eigenvector #1 Eigenvector #2 Eigenvector #3 Eigenvector #4 21 October1000 20082000 1000 2000CLARREO:1000 Feedbacks2000 and1000 Sensitivity2000 20 The Climate Benchmark −1 d=cos Re λ[]cos2 cos() λ− 1 φ cos 1 φ+ 2 1 sin φ 2 sin φ • Lat-lon grid is standardized 6816= . 74 km • Uncertainty derived fom r t testsdenpenedin 62º26.53" N 114º23.85" W 46º57.00" N 7º27.00" E 21 October 2008 CLARREO: Feedbacs and Sensitivityk 12 How Not to Monitor Climate… • Tape measure calibration is unstandardized • Tape measure subject to failure (by scissors) 21 October 2008 CLARREO: Feedbacks and Sensitivity 22 How Does Spectral IR Test GCMs? 0.0 rad] -6 -0.2 Radiance [10 Δ -0.4 0 500 1000 1500 2000 Frequency [cm-1] 21 October 2008 CLARREO: Feedbacks and Sensitivity 23 How Does Spectral IR Test GCMs? 0.0 rad] -6 -0.2 Radiance [10 Δ -0.4 0 500 1000 1500 2000 Frequency [cm-1] 21 October 2008 CLARREO: Feedbacks and Sensitivity 24 How Does Spectral IR Test GCMs? Anthropogenic Forcing Climate Response 0.0 0.0 0.0 rad] rad] rad] -6 -6 -6 -0.2 -0.2 -0.2 Radiance [10 Radiance [10 Radiance [10 Δ Δ Δ -0.4 -0.4 0 500 1000 1500 2000 0 500 1000 1500 2000 Frequency [cm-1] Frequency [cm-1] -0.4 0 500 1000 1500 2000 Frequency [cm-1] 21 October 2008 CLARREO: Feedbacks and Sensitivity 25 How Does Spectral IR Test GCMs? (3) 0.0 rad] -6 Radiance [10 -0.2 Δ -0.4 500 1000 1500 2000 Frequency 21 October 2008 CLARREO: Feedbacks and Sensitivity 26 How Does Spectral IR Test GCMs? (3) 0.0 rad] -6 Radiance [10 -0.2 Δ -0.4 500 1000 1500 2000 Frequency 21 October 2008 CLARREO: Feedbacks and Sensitivity 27 How Does Spectral IR Test GCMs? (3) 0.0 rad] -6 Radiance [10 -0.2 Δ -0.4 500 1000 1500 2000 Frequency 21 October 2008 CLARREO: Feedbacks and Sensitivity 28 Water Vapor-Longwave Feedback Precision After 20 Years Linear Trend Analysis Correlation Analysis (W m-2 K-1) (W m-2 K-1) Truth Data Truth Data GFDL CM2.0 3.30 ± 1.85 3.20 ± 1.85 2.75 ± 0.20 2.53 ± 0.18 GISS E-H 2.63 ± 0.81 2.95 ± 0.62 2.61 ± 0.10 2.94 ± 0.12 MIROC3.2 2.81 ± 0.85 2.53 ± 0.62 2.68 ± 0.13 2.49 ± 0.10 ECHAM5 3.14 ± 1.60 3.53 ± 1.81 2.98 ± 0.08 3.36 ± 0.10 CCSM3 2.80 ± 0.92 2.81 ± 0.91 2.66 ± 0.17 2.66 ± 0.16 HadCM3 3.10 ± 1.48 2.65 ± 1.15 2.78 ± 0.09 2.74 ± 0.11 …scales as (Δt)-3/2 …scales as (Δt)-1/2 21 October 2008 CLARREO: Feedbacks and Sensitivity 29 Water Vapor-Longwave Feedback Precision After 20 Years Linear Trend Analysis Correlation Analysis (W m-2 K-1) (W m-2 K-1) Truth Data Truth Data GFDL OptimizationCM2.0 3.30 ± 1.85 : Little3.20 ± 1.85 as of2.75 yet, ± 0.20 but 2.53 ± 0.18 GISS E-Hinclusion2.63 of± 0.81 the spatial2.95 ± 0.62 dimension2.61 ± 0.10 2.94 ± 0.12 MIROC3.2should 2.81yield ± 0.85 substantial2.53 ± 0.62 improvement.2.68 ± 0.13 2.49 ± 0.10 ECHAM5 3.14 ± 1.60 3.53 ± 1.81 2.98 ± 0.08 3.36 ± 0.10 CCSM3 2.80 ± 0.92 2.81 ± 0.91 2.66 ± 0.17 2.66 ± 0.16 HadCM3 3.10 ± 1.48 2.65 ± 1.15 2.78 ± 0.09 2.74 ± 0.11 …scales as (Δt)-3/2 …scales as (Δt)-1/2 21 October 2008 CLARREO: Feedbacks and Sensitivity 30 Accuracy Requirements, Detection Times • With observations traceable to international standards, one evaluates the uncertainty (accuracy) of individual measurements in a timeseries.
Load more

Recommended publications
  • Kurt Thome, Calibration Accuracy for Cimate-Quality Data Sets From
    Calibration Accuracy for Cimate-Quality Data Sets from Space Observations K. Thome1, Mlynczak2, B. Wielicki2, D. Young2 1NASA/GSFC 2NASA/LaRC NEWRAD Maui, Hawaii September 19, 2011 Talk overview Climate quality data records require high accuracy and SI traceability One goal is to understand climate change projections Summarize methods to determine the observing requirements Reflected solar and IR SI-traceable uncertainty Temperature to 0.07 K (k=2) Reflectance to 0.3% (k=2) Instrument approaches and calibration methods Climate model test and decadal change Most powerful test of climate model prediction accuracy is decadal change observations Accuracy required at large time and space scales Zonal annual, not instantaneous field of view Very different than typical process missions Questions are How long a data record is needed? What variables are key? What accuracy relative to perfect observing system is needed? How are the above determined? Climate Science and Observations Process Inter-calibration Climate Change Calibration 1st Approach Climate Benchmarks IPCC Uses 5yr running mean climate data Climate science requirements Observations and climate model predictions combined to develop requirements Satellite Multi-year Climate High Spectral Interannual Retrieval and CLARREO CLARREO Climate Resolution Simulated Weather Instantaneous Gridded Science Data Radiative Spectral Instantaneous Spectra Monthly Requirements Model Variability OSSEs Observations Spectra Observing System Simulation Experiments (OSSES) Multi- Climate AIRS/IASI
    [Show full text]
  • CLARREO Pathfinder/VIIRS Intercalibration
    remote sensing Article CLARREO Pathfinder/VIIRS Intercalibration: Quantifying the Polarization Effects on Reflectance and the Intercalibration Uncertainty Daniel Goldin1,2,*, Xiaoxiong Xiong 3, Yolanda Shea 2 and Constantine Lukashin 2 1 Science Systems and Applications, Inc., (SSAI), Hampton, VA 23666, USA 2 NASA Langley Research Center, Hampton, VA 23666, USA 3 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA * Correspondence: [email protected] Received: 2 July 2019; Accepted: 12 August 2019; Published: 16 August 2019 Abstract: Atmospheric scattering and surface polarization affect radiance measurements of polarization-sensitive instruments on orbit. Neglecting the polarization effects may lead to an inaccurate radiance/reflectance determination and underestimated radiance/reflectance uncertainty. Of the two instruments, CERES and VIIRS, slated to be intercalibrated by the CLARREO Pathfinder (CPF), the latter is known to be sensitive to polarization. The Pathfinder mission is tasked with accurately determining the uncertainty contribution of polarization and will provide the benchmark for the determination of the polarization correction factor for polarization-sensitive instruments. In this article, we show the formalism necessary to correct the reflectance for sensitivity to polarization after the CLARREO Pathfinder/VIIRS intercalibration, as well as the associated polarization uncertainty contribution to the overall intercalibrated reflectance error. To illustrate its usage, the formalism is applied to three dominant scene types. Keywords: VIIRS; CLARREO Pathfinder; CPF; intercalibration; polarization; reflectance; reflectance correction; polarization uncertainty 1. Introduction CLARREO Pathfinder and VIIRS Missions In 2007, the National Research Council’s Earth Science Decadal Survey recommended the implementation of Climate Absolute Radiance and Refractivity Observatory (CLARREO) [1] as a Tier-1 mission.
    [Show full text]
  • Multi-Instrument Inter-Calibration (MIIC) System
    remote sensing Article Multi-Instrument Inter-Calibration (MIIC) System Chris Currey 1,*, Aron Bartle 2, Constantine Lukashin 1, Carlos Roithmayr 1 and James Gallagher 3 1 NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681, USA; [email protected] (C.L.); [email protected] (C.R.) 2 Mechdyne Corporation, Virginia Beach, VA 23462, USA; [email protected] 3 OPeNDAP, Inc., Butte, MT 59701, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-757-864-4691 Academic Editors: Richard Müller and Prasad S. Thenkabail Received: 21 September 2016; Accepted: 25 October 2016; Published: 3 November 2016 Abstract: In order to have confidence in the long-term records of atmospheric and surface properties derived from satellite measurements it is important to know the stability and accuracy of the actual radiance or reflectance measurements. Climate quality measurements require accurate calibration of space-borne instruments. Inter-calibration is the process that ties the calibration of a target instrument to a more accurate, preferably SI-traceable, reference instrument by matching measurements in time, space, wavelength, and view angles. A major challenge for any inter-calibration study is to find and acquire matched samples from within the large data volumes distributed across Earth science data centers. Typically less than 0.1% of the instrument data are required for inter-calibration analysis. Software tools and networking middleware are necessary for intelligent selection and retrieval of matched samples from multiple instruments on separate spacecraft. This paper discusses the Multi-Instrument Inter-Calibration (MIIC) system, a web-based software framework used by the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder mission to simplify the data management mechanics of inter-calibration.
    [Show full text]
  • Climate Absolute Radiance and Refractivity Observatory (Clarreo)
    https://ntrs.nasa.gov/search.jsp?R=20160006426 2019-08-31T03:14:07+00:00Z View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by NASA Technical Reports Server CLIMATE ABSOLUTE RADIANCE AND REFRACTIVITY OBSERVATORY (CLARREO) J. Leckey a, * a National Aeronautics and Space Agency (NASA) Langley Research Center (LARC), Bldg. 1202 MS 468, Hampton, Virginia 23681 USA – [email protected] THEME: Atmosphere, weather and climate (ATMC) KEY WORDS: Infrared, Far-Infrared, Radiance, Climate, Atmosphere ABSTRACT: The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a mission, led and developed by NASA, that will measure a variety of climate variables with an unprecedented accuracy to quantify and attribute climate change. CLARREO consists of three separate instruments: an infrared (IR) spectrometer, a reflected solar (RS) spectrometer, and a radio occultation (RO) instrument. The mission will contain orbiting radiometers with sufficient accuracy, including on orbit verification, to calibrate other space-based instrumentation, increasing their respective accuracy by as much as an order of magnitude. The IR spectrometer is a Fourier Transform spectrometer (FTS) working in the 5 to 50 µm wavelength region with a goal of 0.1 K (k = 3) accuracy. The FTS will achieve this accuracy using phase change cells to verify thermistor accuracy and heated halos to verify blackbody emissivity, both on orbit. The RS spectrometer will measure the reflectance of the atmosphere in the 0.32 to 2.3 µm wavelength region with an accuracy of 0.3% (k = 2). The status of the instrumentation packages and potential mission options will be presented.
    [Show full text]
  • Climate Quality Broadband and Narrowband Solar Reflected Radiance Calibration Between Sensors in Orbit
    Climate Quality Broadband and Narrowband Solar Reflected Radiance Calibration Between Sensors in Orbit Bruce A. Wielicki, David R. Doelling, David F. Young, Norman G. Loeb, Donald P. Garber, and David G. MacDonnell NASA Langley Research Center, Hampton, VA 23681 ABSTRACT effective for improving past data sets like ISCCP. Results for intercalibration of geostationary imagers to CERES As the potential impacts of global climate change become using an entire month of regional nearly coincident data more clear [1], the need to determine the accuracy of demonstrates new approaches to constraining the calibration climate prediction over decade-to-century time scales has of current geostationary imagers. become an urgent and critical challenge. The most critical The new Decadal Survey Mission CLARREO is tests of climate model predictions will occur using examining future approaches to a "NIST-in-Orbit" approach observations of decadal changes in climate forcing, of very high absolute accuracy reference radiometers that response, and feedback variables. Many of these key climate cover the full solar and infrared spectrum at high spectral variables are observed by remotely sensing the global resolution but at low spatial resolution. Sampling studies distribution of reflected solar spectral and broadband have shown that a precessing CLARREO mission could radiance. These "reflected solar" variables include aerosols, calibrate other geo and leo reflected solar radiation and clouds, radiative fluxes, snow, ice, vegetation, ocean color, thermal infrared sensors. and land cover. Achieving sufficient satellite instrument accuracy, stability, and overlap to rigorously observe Index Terms— Meteorology, Remote Sensing, decadal change signals has proven very difficult in most Calibration. cases and has not yet been achieved in others [2].
    [Show full text]
  • NASA's Plans for Greenhouse Gas Observations from Space
    NASA’s Plans for Greenhouse Gas Observations from Space Ken Jucks Program Scientist for OCO-2, OCO-3, GeoCARB NASA Headquarters PREFIRE (2) (Pre)Formulation NASA Earth Science PACE* Implementation TSIS-2 GeoCARB Primary Ops Missions: Present through 2023 TROPICS (12) Extended Ops MAIA Landsat 9 Sentinel-6A/B NISAR ISS Instruments SWOT LIS, SAGE III, TEMPO InVEST/CubeSats TSIS-1, OCO-3, ECOSTRESS, GEDI, CLARREO-PF*, EMIT ICESat-2 RAVAN GRACE-FO (2) CYGNSS (8) RainCube TEMPEST-D JPSS-2 Instruments CubeRRT CSIM SMAP OMPS-Limb CIRiS NISTAR, EPIC Landsat 7 HARP (DSCOVR /NOAA) (USGS) SORCE, SNoOPI TCTE (NOAA) Suomi NPP Terra HyTI (NOAA) CTIM NACHOS Landsat 8 Aqua (USGS) CloudSat CALIPSO GPM Aura OSTM/Jason-2 (NOAA) OCO-2 04.09.19 *Recommended for termination in the FY20 Presidential Budget Request released March 11, 2019 OCO-2 and plans for the future • OCO-2 has officially outlived its planned lifetime. • OCO-2 WILL be proposing for continuation of ”Extended” operations as part of the NASA Earth Science Senior Review process in 2020. • The assumption is that, if the satellite is still returning science quality data, NASA will approve continued operations. • Science quality data are demonstrated BEST by published papers! • OCO-2 went through this process in 2017 as well. • The observatory is now starting to show its age. • A reaction wheel is quickly “dying”. The spacecraft can operate in star tracker mode to maintain pointing capability. OCO-3 is NOW on the ISS • OCO-3 is approaching the end of its decontamination phase of IOT. • All systems tested to date have been nominal.
    [Show full text]
  • Catalogue of Satellite Instruments
    Instrument & agency (& any partners) Missions Status Type Measurements & applications Technical characteristics 3MI METOP-SG A1, METOP- Being developed Atmospheric Measure aerosol parameters, air quality index, surface albedo, Waveband: VIS-SWIR: 12 channels between 0.41 µm to 2.1 SG A2, METOP-SG A3 chemistry cloud information µm Multi-Viewing Multi-Channel Multi- Spatial resolution: 4km Polarisation Imaging Swath width: 2200x2200 km for the VNIR channels EUMETSAT (ESA) 2200 x 1100 km for the SWIR channels Accuracy: ABI GOES-R, GOES-S, Being developed Imaging multi- Detects clouds, cloud properties, water vapour, land and sea Waveband: 16 bands in VIS, NIR and IR ranging from 0.47 GOES-T, GOES-U spectral surface temperatures, dust, aerosols, volcanic ash, fires, total µm to 13.3 µm Advanced Baseline Imager radiometers (vis/IR) ozone, snow and ice cover, vegetation index. Spatial resolution: 0.5 km in 0.64 µm band; 2.0 km in long wave IR and in the 1.378 µm band; 1.0 km in all others NOAA Swath width: Accuracy: Varies by product ACC Swarm Operational Precision orbit and Measurement of the spacecraft non-gravitational accelerations, Waveband: N/A space environment linear accelerations range: +/- 2*10-4 m/s2; angular measurement Spatial resolution: 0.1 nm/s2 Accelerometer range: +/- 9.6* 10-3 rad/s2; measurement bandwidth: 10-4 to 10-2 Swath width: N/A Hz; Linear resolution: 1.8*10-10 m/s2; angular resolution: 8*10-9 Accuracy: overall instrument random error: <10 - 8 m/s2 ESA rad/s2. ACE-FTS SCISAT-1 Operational Atmospheric Measure and understand the chemical processes that control the Waveband: SWIR - TIR: 2 - 5.5 µm, 5.5 - 13 µm (0.02 cm-1 chemistry distribution of ozone in the Earth's atmosphere, especially at high resolution) Atmospheric Chemistry Experiment (ACE) altitudes.
    [Show full text]
  • 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.
    [Show full text]
  • DRAFT Decadal Survey (DS) CLARREO Workshop Report Edited
    DRAFT Decadal Survey (DS) CLARREO Workshop Report Edited by: Donald E. Anderson NASA, MAP Program Manager October 10, 2007 Table of Contents 1. Decadal Survey (DS) CLARREO Statement 2. Introduction 3. Science Supported by DS CLARREO mission 4. Breakout Summaries Solar Backscatter IR emitted 5. Potential Partnerships 6. Challenges Website: http://map.nasa.gov/clarreo.html 1. Decadal Survey (DS) CLARREO Statement CLARREO Mission Statement: The Climate Absolute Radiance and Refractivity Observatory (CLARREO) will provide a benchmark climate record that is global, accurate in perpetuity, tested against independent strategies that reveal systematic errors, and pinned to international standards. Appendix A provides the complete statement of the Decadal Survey CLARREO mission. 2. Introduction The NASA CLARREO workshop held July 17-19 was one of four NASA workshops help in late June and July, 2007 to review the first four DS synthesized missions. As noted by the DS, these mission concepts are at different stages of maturity and the intent of the workshops was to help NASA organize its approach to implementation of DS recommendations. DS thematic panels developed 35 missions from 101 RFIs, from which the DS Executive Committee synthesized 17 missions, with suggested order. CLARREO was identified among the first four missions to be considered. Goals of the workshops include: (1) Identify science goals that lead to specific mission requirements/definition, and noting compromises made during the mission synthesis that may lead to mission constraints. (2) Identify related satellite instrument measurements available during these missions from research and operational missions either necessary for mission success or complimentary. (3) Assess the impact of NPOESS de-manifested missions/measurements, especially if multi-mission ‘constellation’ measurements are necessary to satisfy science goals.
    [Show full text]
  • Bringing Disciplines Together Clarreo: by DAVID YOUNG Photo Credit: NASA Been Possible with Existing Instruments
    48 | ASK MAGAZINE | STORY BRINGING DISCIPLINES TOGETHER CLARREO: BY DAVID YOUNG ASK MAGAZINE | 49 CLARREO, the Climate Absolute Radiance and Refractivity Observatory, is an Earth- science satellite mission in pre-Phase A (conceptual study) that is being designed to capture critical climate-change data much more precisely than has been possible with existing instruments. Its spectrometers, sensitive to the full range of infrared and visible radiation, will improve the accuracy of measurements of all the radiation leaving Earth by a factor of two to ten. That accuracy and the mission’s ability to measure trends over a decade or more could help scientists know whether climate change will be less or more severe than expected as much as two decades earlier than current data allow. This could be a key determinant for decisions concerning our nation’s response to changes in climate. So it’s not surprising that the 2007 decadal survey of “Earth Science and Applications from Space” considered CLARREO one of four high-priority Earth-science missions. In response to the survey, a small team of scientists was formed at Langley Research Center to define the mission. In early 2009, we gathered a full-fledged preformulation team including scientists, systems analysts, discipline engineers, and business analysts at Langley along with smaller teams at Goddard Space Flight Center and the Jet Propulsion Laboratory and about ten external organizations with the goal of developing a feasible concept for CLARREO. Bringing the Team Together Having worked on other NASA science missions, including CERES (Clouds and the Earth’s Radiant Energy System) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), mission scientist Bruce Wielicki, mission formulation manager Steve Sandford, and I were well aware of a familiar pitfall—that the CLARREO scientists would want only the best possible data regardless of the practical difficulties involved in getting it.
    [Show full text]
  • CLARREO Pathfinder Mission: Calibrating Climate Observing Systems of the Future
    CLARREO Pathfinder Mission: Calibrating Climate Observing Systems of the Future Marty Mlynczak NASA Langley Research Center FORUM Workshop Florence, Italy – October 23, 2018 CLARREO Pathfinder Mission: Calibrating Climate Observing Systems of the Future Bruce A. Wielicki, C. Lukashin, Y. L. Shea, G. Fleming NASA Langley Research Center G. Kopp, P. Pilewskie, P. Smith, G. Ucker University of Colorado – LASP K. Thome, NASA Goddard Space Flight Center R. Cooke, Resources for the Future S. Limaye, Univ. of Wisconsin A. A. Golub, American University FORUM Workshop Florence, Italy – October 23, 2018 OUTLINE • The CLARREO Concept • The Climate Sensitivity Challenge • The Cloud Feedback Challenge • Reducing uncertainty through improved calibration of sensors • Economic value of CLARREO and a dedicated Climate Observing System • The CLARREO Pathfinder Mission • Summary What is CLARREO? • Climate Absolute Radiance and Refractivity Observatory • CLARREO • Fundamental Precept – improved sensor calibration shortens time to detect trends in key climate parameters • Rather than try to build every sensor to climate accuracy, place a set of highly accurate sensors in orbit to calibrate all others • The economic value of CLARREO and a dedicated Climate Observing System is in the Trillions of dollars (U. S.) (1 $T = 1012 $) • CLARREO • Is not a radiation budget mission, nor a cloud property mission, nor a replacement for CERES, VIIRS, MODIS, CrIS, etc. What is CLARREO? CLARREO’s Overarching Objective To dramatically improve calibration of orbiting sensors so as To reduce time to detect trends in forcings and feedbacks and thereby Substantially reduce uncertainty in climate sensitivity for The benefit of all humanity The Climate Sensitivity Challenge • Climate sensitivity uncertainty remains the most critical limit to future climate prediction impacts and costs.
    [Show full text]
  • CLARREO APPROACH for REFERENCE INTERCALIBRATION of REFLECTED SOLAR SENSORS 6763 Relevant to Climate Change Observations
    6762 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 10, OCTOBER 2014 CLARREO Approach for Reference Intercalibration of Reflected Solar Sensors: On-Orbit Data Matching and Sampling Carlos M. Roithmayr, Constantine Lukashin, Paul W. Speth, Greg Kopp, Kurt Thome, Bruce A. Wielicki, and David F. Young Abstract—The implementation of the Climate Absolute Ra- We conclude that with the CLARREO RSS in a polar 90◦ in- diance and Refractivity Observatory (CLARREO) mission was clination orbit at a 609-km altitude, estimated intercalibration recommended by the National Research Council in 2007 to pro- sampling will limit the uncertainty contribution from data match- vide an on-orbit intercalibration standard with accuracy of 0.3% ing noise to 0.3% (k =2)over the climate autocorrelation time (k =2)for relevant Earth observing sensors. The goal of ref- period. The developed orbital modeling and intercalibration event erence intercalibration, as established in the Decadal Survey, prediction will serve as a framework for future mission operations. is to enable rigorous high-accuracy observations of critical cli- mate change parameters, including reflected broadband radiation Index Terms—Data sampling, intercalibration, orbital simula- [Clouds and Earth’s Radiant Energy System (CERES)], cloud tion, radiometry. properties [Visible Infrared Imaging Radiometer Suite (VIIRS)], and changes in surface albedo, including snow and ice albedo I. INTRODUCTION feedback. In this paper, we describe the CLARREO approach for performing intercalibration
    [Show full text]