# Presentation: 935815

## CLARREO Langley IR Study:Numerical simulation

- Bruce Wielicki1, Seiji Kato1, Fred Rose2,
- Dave Kratz1, Xu Liu1, Sunny San-Mack2,
- and Walt Miller2
- 1NASA Langley Research Center
- 2Science Systems & Applications Inc.
**the First CLARREO Mission Study Team Meeting**
- Newport News, Virginia
- April 30 to May 2, 2008

## Ties to Traceability Matrix

**Climate model OSSEs ***Optimal for studies related to impact of benchmark spectra on climate change detection, attribution, prediction accuracy, decade to 100 year time scales, clear vs all-sky simulations** *
**Observation + RTM spectra simulations ***Optimal for studies of field of view (spatial variability < 100km scale), realistic interannual variability, realistic clouds, aerosols (e.g. A-train), issues of sampling/swath for a range of time/space scales, diurnal sampling studies.*

## Ties to Traceability Matrix

**LaRC IR Studies Summary:**
- A-train + RTM for most realistic aerosol/cloud variability
***Field of View, Cirrus + Trade Cu effects, Cloud Overlap, Spectral Coverage (e.g. Far-IR)  ***
- Line-by-Line studies of linearity of distributions of atmospheric properties to averaged spectra
***Benchmark spectra relation to underlying climate signals***
- Intercalibration of broadband and imagers in solar and infrared (last workshop).
***Field of view, Swath, Orbit, Pointing ability, knowledge, control***
- Full Swath vs Nadir Only Sampling using CERES surface, cloud, aerosol, radiation data.
***Swath, Number of Orbits***
- Diurnal Sampling studies for climate change: CERES + 3-hrly geo data, vs CERES Terra 1030LT only
***Orbits, Number of Orbits***

## Method

- Step 1: Use simple clouds and atmosphere and a) test sensitivity of the TOA effective temperature (spectrum) to atmospheric and cloud properties b) test if we can infer atmospheric property changes from time series of spectrum anomalies.
- Step 2: Use A-train realistic cloud fields, test our results from Approach 1.

## Realistic Cloud fields

- Cloud fields are derived from CALIPSO, CloudSat, and MODIS (C3M), including thin cirrus, boundary layer cumulus, multi-layer clouds, and aerosols.
- C3M product contains merged CALIPSO, CloudSat, CERES and MODIS data, SW and LW TOA flux from CERES, and flux profiles computed with CALIPSO, CloudSat, and MODIS clouds and aerosols (work is funded by NASA Energy Water Cycle Study (NEWS)

## A-train

## CALIPSO CloudSat MODIS collocation

- From S. Sun-Mack

## CALIPSO CloudSat MODIS merging with CERES

## Merged clouds

##  Cloud and aerosol fields

- from 532 nm

## Cloud cover over CALIPSO-CloudSat ground-track

- 2.7%

- 6.1%

- 11.2%

- 15.0%

- Clear-sky

## Clear scenes (almost)

## Study with CALIPSO-CloudSat cloud fields

- Compute TOA spectra with MODTRAN, Principal Component Radiative Transfer Model (PCRTM), and lbl code.
- Examine whether or not atmospheric property changes can be detected from TOA spectra.
- Examine whether or not signals can be separated when more than one variables change at the same time.

## Study with simple cloud fields

## Averaging spectral data

- Which atmospheric variables give a linear response to the brightness temperature at TOA?
- Look for the mean spectra = spectrum of the mean
- How does the linearity depend on the mean atmospheric state?

## Hypothetical atmospheric property change

- Assume a distribution of a cloud or an atmospheric property x at time t1.
- Perturb the distribution for the distribution of x at time t2.
- Compute mean spectrum from the distribution at t1 and t2 separately by weighting the spectrum by frequency of occurrence.
- Compute the difference of the mean spectrum at t1 and t2:
- Compute the mean of x at t1 and t2, two spectra using two mean values, and the difference

## Simple study with MODTRAN

- Mean
- 0.98
- 1.01

- Mean
- 10 km
- 12 km

## Sensitivity

- Cloud height
- Emissivity = 1-exp(-tau*0.5)
- Water vapor Scale height
- Lapse rate (0 - 15 km) --> T(z)
- Skin temperature
- Surface air temperature (fixed QSH, RHSFC) -->H2O(z)
- Surface relative humidity (fixed QSH, TSFC) -->H2O(z)

## Linearity

- Cloud emissivity 0.4+-0.01

- Surface skin temp. 300+-1K

- Cloud height 10+-1 km

- Surface air temp. 300+-5K

## Langley LW Line by Line Modeling

## Strategy

- Use LBL code to validate MODTRAN and PCRTM (especially spectra with clouds).
- MODTRAN and PCRTM run faster than LBL. PCRTM is more flexible with inputs.

## Slide 23

## Slide 24

## Slide 25

## Slide 26

## Slide 27

## Slide 28

## Slide 29

## Slide 30

## Slide 31

## Slide 32

## Slide 33

## Slide 34

## Slide 35

## Slide 36

## Slide 37

## Slide 38

## Slide 39

## Slide 40

## Diurnal Cycles: Full 3-hourly vs Terra 1030LT onlyIs interannual variability in regional, zonal, and global fluxes captured accurately by just Terra 1030LT vs full 3-hourly data?How do the differences compare to expected climate forcing and response?GEO = CERES + GEO = 3-hourly DataNon-GEO = CERES Terra only = 1030 AM/PM only

## Tropical and Global Mean Effect of Diurnal Cycle: Very Small
GEO is CERES + 3-hourly Geo Diurnal Cycle, nonGEO = CERES Terra Only

***GEO is CERES + 3-hourly Geo Diurnal Cycle, nonGEO = CERES Terra Only***

## Tropical and Global Mean Effect of Diurnal Cycle: Very Small

***GEO is CERES + 3-hourly Geo Diurnal Cycle, nonGEO = CERES Terra Only***

## Jan 2001 De-seasonalized SW Flux Anomaly Relative to 2001-2005 Avg
(CERES Terra plus 3-hourly geostationary data for diurnal cycle)

***(CERES Terra plus 3-hourly geostationary data for diurnal cycle)***

## Jan 2001 De-seasonalized SW Flux Anomaly Relative to 2001-2005 Avg
(CERES Terra (1030LT) only for diurnal cycle)

***(CERES Terra (1030LT) only for diurnal cycle)***

## Jan 2001 De-seasonalized SW Flux Anomaly Relative to 2001-2005 Avg
(With and Without Geo: Effect of Diurnal Cycle is Small)

***(With and Without Geo: Effect of Diurnal Cycle is Small)***

## Jan 2001 De-seasonalized LW Flux Anomaly Relative to 2001-2005 Avg
(CERES Terra plus 3-hourly geostationary data for diurnal cycle)

***(CERES Terra plus 3-hourly geostationary data for diurnal cycle)***

## Jan 2001 De-seasonalized LW Flux Anomaly Relative to 2001-2005 Avg
(CERES Terra (1030LT) only for diurnal cycle)

***(CERES Terra (1030LT) only for diurnal cycle)***

## Jan 2001 De-seasonalized LW Flux Anomaly Relative to 2001-2005 Avg
(With and Without Geo: Effect of Diurnal Cycle is Small)

***(With and Without Geo: Effect of Diurnal Cycle is Small)***

## Conclusions

**Conclusions**

- Regional, Zonal, Global monthly and interannual variations of SW and LW fluxes examined
- Compared 3-hourly vs Terra sunsynch 1030LT only
- Used all-sky fluxes to include both clear and cloudy diurnal cycles (land, stratus, convection, desert, etc)
- While regional differences can be large in the mean fields (“true” vs 1030LT), global differences are very small.
- For anomalies (monthly, interannual, regional, zonal, global) “true” vs 1030LT showed very small differences, with the solar flux effects about 3 times as large as infrared
- LW and SW anomalies from “true” vs 1030LT anomalies are much less than natural variability

## Conclusions

**Conclusions**

- These tests should be repeated in climate model OSSEs for CLARREO spectral radiances
- Extend trends from global to zonal and regional and examine signal to noise for 3hrly vs 1030LT only.
- Why is the diurnal cycle interannual variation so small? Most likely the physics of climate change:
- diurnal cyles are driven by the huge solar forcing from day to night: ~ 340 W/m2 avg absorbed at the surface during day, vs 0 at night.
- there are no global change forcings (greenhouse gas driven) that are large diurnal forcing.  largest is aerosol forcing: ~1 W/m^2 over 40 years vs 320 W/m^2 for solar forcing: i.e. 1 part in 300.
- For a diurnal cycle of temperature of 10K: 10K/300 = 0.033K diurnal cycle forcing over 40 years, or 0.03 out of 0.8K from greenhouse gas warming in same 40 years: 5% of the signal, most of which will be captured at 1030 AM/PM.

## Future task and schedule

- Perform more simple tests with MODTRAN (e.g. different atmosphere, cloud microphysics) [by Aug. 08].
- Set up radiative transfer codes [by Fall 08].
- Use CALIPSO-CloudSat cloud fields and models (MODTRAN, PCRTM, lbl), derive cloud statistics [by Fall 08].
- Simulate A-train TOA spectra [by Spring 09]
- Perform a principal component analysis and/or matrix inversion to unscramble atmospheric property changes using zonal monthly mean spectrum anomalies (land and ocean separately) [by Spring 09].

## Future task

- Compare the results for imposed atmospheric property changes (especially boundary layer structure) and assess the error [by Fall 09].
- Spatial sampling test. (Nadir vs. full swath) [by Fall 09].
- Diurnal sampling and CLARREO precessing orbit sampling [by Fall 09].
- Estimate the noise introduced from spatial and temporal CLARREO sampling and assess the magnitude compared with signals to be detected [by Fall 09].

## Back-ups

## CALIPSO-CloudSat-CERES-MODIS Merged Product

- Cloud overlap profiles
- CALIPSO and ClousSat
- Cloud and aerosol
- properties
- CALIPSO, CloudSat,
- and MODIS
- TOA Irradiances
- CERES
- Irradiance profiles
- Radiative transfer model

- Cloud layers

- Aerosol layers

## Compute TOA spectra with MODTRAN, PCRTM, and lbl code. 
Principal component analysis of time series of monthly spectrum anomalies

Or from Linear matrix inversion of spectra anomalies

Can we derive atmospheric property change from monthly regional mean spectral data?

- Principal component analysis of time series of monthly spectrum anomalies
- Or from Linear matrix inversion of spectra anomalies
- Can we derive atmospheric property change from monthly regional mean spectral data?

- Study with CALIPSO-CloudSat cloud fields

## Zonal Monthly Mean Uncertainty due to nadir only sampling

- Zonal Monthly Flux Uncertainty
- 2 to 3 W m^-2 for SW
- ~1 W m^-2 for LW

- RMS difference of zonal mean instantaneous TOA flux

## Climate study using spectral data

- Testing climate models (Goody et al. BAMS 1998).
- Correlation of (monthly) mean spectrum anomalies with time-independent model predicted change (Goody et al. 1995; *Harries et al. 1998, JRL*).
- Principal component analysis of monthly mean spectrum anomalies (e.g. *Rabbette and Pilewskie 2001JGR; Huang et al 2003 ICARUS*)

## PCRTM