# Presentation: 146994

## Review: CCSP Chapter 3Climate Quality Observations

**Bruce Wielicki**
**NASA Langley Research Center**

## Satellite Climate Observations

- No complete climate observation system yet exists, nor does the CCSP create one: how do we accomplish it?
- No complete climate observation requirements have yet been defined.  Not a clear plan to provide them.
- Add the 10 Satellite Climate Observation Principles (GCOS)
- Need to add a missing 21st climate observing principle:*independent climate quality observations for each parameter*
- NPOESS is a good example of not reaching climate quality yet in calibration/stability of many parameters.
- 4-D assimilation of climate data will be very powerful for oceans, air  temperature, water vapor, winds.  But much less so for aerosols, clouds&radiation, and precipitation: the largest uncertainties in forcing, feedback, response respectively.
- Tuning current data/plans will not create a rigorous climate observing system: a major effort is needed and costs will be significant.  Perhaps as large as the current weather system: need better accuracy, stability, overlap, and more parameters.

## GCOS Climate Observation Principles

***Satellite systems for monitoring climate should adhere to the following specific principles: (in addition to the 10 in CCSP document)***
- Rigorous station keeping should be maintained to minimize orbital drift.
- Overlapping observations should be ensured for a period sufficient to determine inter-satellite biases.
- Satellites should be replaced within their projected operational lifetime (rather than on failure) to ensure continuity (or in-orbit replacements should be maintained).
- Rigorous pre-launch instrument characterization and calibration should be ensured.
- Adequate on-board calibration and means to monitor instrument characteristics in space should be ensured.
- Development and operational production of priority climate products should be ensured.
- Systems needed to facilitate user access to climate products, metadata and raw data, including key data for delayed-mode analysis, should be established and maintained.
- Continuing use of still-functioning baseline instruments on otherwise de-commissioned satellites should be considered.
- The need for complementary in-situ baseline observations for satellite measurements should be appropriately recognized.
- Network performance monitoring systems to identify both random errors and time-dependent biases in satellite observations should be established.

## The missing 21st Climate Observation Principle

- Independent methods of observation (instrument types) and analysis of data are required
- NIST calibration example: uncertainties in calibration references are only known when independent national labs intercompare, and usually find larger uncertainty than predicted.
- Policy impacts are so large that independent verification of any climate surprises will be required.
- Independent observations of each key parameter *at climate accuracy* must be made using different observing methods: infrared, microwave, radiosonde, radar, lidar, gps, etc.
- Independent analysis is required to calibrate analysis codes and methods: *all code has errors*: *how big are they?*
- SST and T(z) already meet this requirement: infrared, microwave from satellite, buoys and rawindsonde from surface.
- Clouds, aerosols, radiation, precipitation do not yet meet this requirement.  Key uncertainties for forcing, feedback, response.

## The missing 21st Climate Observation Principle

- Examples of changes that would be required for independent verification of aerosols, clouds, radiation:
- extension of surface BSRN radiation, AERONET aerosol, and surface lidar/radar sites (e.g. ARM) colocated in all major climate regimes: many of these networks are ad-hoc funded (BSRN, AERONET, lidar sites)
- global active lidar and cloud radar observations in space to be independent measure of cloud/aerosol from passive imagers and polarimeter: e.g.: prototypes in Cloudsat and Calipso 2004 NASA missions: but no operational plan yet.
- greatly improved measures of cloud ice water path required
- both spectral and broadband measurements of solar and thermal infrared radiation from space and ground sites. e.g.: broadband radiation from space as well as spectrally resolved across the entire broadband spectrum (never been done in solar or IR: miss 1/2 of IR).
- because of very large cloud variability: sufficient sampling, diurnal cycle coverage, and accuracy across all cloud types will be very difficult and expensive.
- Summary: minimum of 4 climate data sets for each parameter:           *2 instrument types times 2 data analysis versions.*

## Climate Surprises: Tropical Mean (20S-20N) TOA LW Flux

***How do we verify it independently at climate accuracy?***

## NPOESS: example challenges still to be faced

- Accuracy/stability 10 times more difficult than weather: 0.1K not 1K.
- Overlapping observations not supported: launch after instrument failure: needs to be hot spares in orbit intercalibrated BEFORE failures.  Critical for most climate records from space.  No current analysis of gap risk versus time in the climate record plan.
- Deep space lunar calibration for solar reflectance imagers (VIIRS) and zero level for thermal instruments are fundamental for climate calibration: not planned.
- Algorithms and instrument calibration currently done under “black box” delivery: need science review teams with career on the line, not award fee.  And decisions drive schedule and cost of NPOESS
- Climate data quality is a unique merger of research accuracy with operations consistency and completeness: NPOESS designed for later.
- All of the above suggests that successful climate quality NPOESS data will REQUIRE climate accuracy/stability be elevated to the level of an NPOESS CRITICAL requirement: don’t launch until its met.  It drives cost and schedule.

## EOS to

**to determine probability of failure with time from launch.  Arrow lengths**
**are design mission lifetimes.**

***EOS to***
***NPOESS***
***transition***
***gap risk.***

***What is an ***
***acceptable***
***climate ***
***record ***
***gap risk?***
***  ******< 10%?***

## Climate Observations and 4-D Assimilation

- A very good direction to push, but it won’t solve many climate observation problems with data gaps/accuracy/stability
- Will work best for classic dynamics: temperature, wind, most humidity fields.
- Biggest problems will be the physics that is poorest in the 4-D models and climate models: aerosols, clouds, precip, radiation.
- aerosols: composition, rainout, absorption, effect on cloud optics and precipitation
- cloud properties: huge scale separation of cloud physics from climate model resolution (10 to 100m vs 100km).
- precipitation: microphysics and large scale separation from physics scale to climate model (100m to 1km vs 100km)
- radiation: 3-D cloud structure vs 1-D climate model, interaction with aerosols, uncertain ice scattering/particle habit, large scale separation as for cloud properties.
- overall: variability commonly exceeds climate desired accuracy by factors of 100 to 1000.

## Summary

- Many general needs mentioned but lacks clear implementation strategy beyond scientific committees.
- No Energy Cycle in the document: even though this is what drives climate change: e.g. no radiation measurement: broadband or spectral
- Responsibility/Accountability is diffused and weak:
- no agency fails if we fail the climate mission
- climate observations are viewed as tuning other research and weather operational data: require unique climate observational  system with its own requirements independent of others.
- must overcome major organizational and resource limits
- who “owns” and pays for the climate observation requirements?
- Its time to admit that we have no rigorous climate observing system either in place or designed or planned.
- If an alien gave us 5 climate models, one of which was perfect: we probably couldn’t tell which one to pick.