Advances from Active Profiling:
Addressing the Cloud Feedback Challenge
Dave Winker1 and Helene Chepfer2
1) NASA LaRC, Hampton, VA
2) LMD, Paris
A-Train Symposium, Pasadena, 21 April 2017
Lessons learned:
• Satellite constellations are feasible and necessary to advance climate science
• Close formation flying can be accomplished and made routine
• Nadir-only sampling is sufficient for many (not all) applications
Multiple A-train synergies:
CALIPSO + CloudSat: cloud profiles, phase, IWC
CALIPSO + CloudSat + MODIS: cloud radiative effects, heating profiles
CloudSat+MODIS: insight into coalescence processes
“An improved ability to quantify vertical profiles of
cloud occurrence and water content”
IPCC AR5: “The application of new observations, such as vertically resolved cloud information from satellites …
has enhanced the ability to evaluate processes in climate models … “
(a) CloudSat/CALIPSO 2B-GEOPROF-LIDAR dataset
(b) liquid water path : microwave radiometer data
ice water path: CloudSat 2C-ICE
c, d) 2B-GEOPROF-LIDAR data set.
(Fig 7.5, IPCC AR5)
Weather State-3
Anvils and Isolated Convection
CP-t diagrams: passive vs. active
(courtesy, Jay Mace)
Evaluation of SEVIRI & AIRS Cloud Height
• Passive retrievals are often confounded by complexities of
the real atmosphere
• Assumptions of forward models are often violated
• Passive sensors retrieve a single cloud altitude, not profile
SEVIRI: homogeneous vs. broken
EBBT
AIRS: opaque singlelayer vs. multi-layer
CO2 slicing
(Di Michele et al., 2012)
An Outstanding Challenge:
Cloud Feedbacks
(Illingworth et al. BAMS 2016)
inter-model DT std dev
Envelope of predicted change in CRE, 2006 to 2100 from eight GCMs
(ECS from 2.7 K to 4.7K).
Cloud
feedbacks
Current uncertainties in climate sensitivity
largely due to uncertainties in modeling
cloud-radiation-climate feedbacks
(Dufresne and Bony, 2008)
Cloud radiative feedbacks due to changes in cloud cover, height, optical depth
Global models consistently show:
1) SW cloud feedbacks primarily due to cloud-amount changes
2) Optical depth feedbacks (phase change?) important at high latitudes
2) LW feedbacks driven by rising altitude, but also changes of amount and OD
(Bony et al.
PNAS 2016)
CFMIP ensemble-mean net cloud feedbacks
(Zelinka et al. 2016)
LW Cloud Feedback
Sign of LW feedback is
robust, magnitude varies
Models predict tropical high
clouds remain isothermal
Decadal mean high cloud pressure height, 2000-2100
upper tropospheric convergence-weighted pressure
(Zelinka and Hartmann, JGR, 2010)
Observational Constraints on Predicted Cloud Changes
Predicted cloud changes are small relative to natural variability
Detection of trend emergence from climate noise requires high accuracy & stability
(Shea et al 2017)
In parallel with the A-Train - “instrument simulators” have been developed in
the modelling community for more consistent comparison of models and
observations
CALIPSO simulator used to identify observable signatures of climate change
(Chepfer et al. 2008)
In CMIP GCMs, tropical cloud rise over
21st century: 0.5 to > 1 km
Observable signal of 21st century cloud rise
from HadGEM2 and CALIPSO simulator:
Estimated time to detect signature of
rise in tropical opaque cloud, combining
future lidar with CALIOP record
(mission overlap not required):
2021 to 2034
Chepfer et al. (in preparation)
Chepfer et al. (2014)
SW Feedbacks: shallow marine clouds
Shallow cloud properties determined by
the balance of competing processes
(Wood, MWR, 2012)
(Nam et al. 2012)
Nadir sampling and cloud cover uncertainties
From statistical sampling theory of Key (1993):
Monthly CA anomalies (60N-60S):
MODIS C6 vs. thresholded CALIOP
rms cloud cover error due to sampling
Time series of subsidence region shallow cloud cover metric:
CALIPSO simulator output from HadGEM2 and CanAM4
Present-day and +4K AMIP experiments assuming linear trend
Model predictions can be discriminated if lidar observations extend to late 2020’s
Chepfer et al. (in preparation)
WCRP: Clouds, Circulation, and Climate
CALIPSO + CloudSat: profiles
of cloud heating rates
• Clouds and circulation will both evolve due to rising GHG
concentrations
• WCRP Grand Challenge has focused attention on the
coupling of clouds and circulation
• Shallow marine clouds coupled to tropical convection via
large-scale circulation
(Haynes et al., 2013)
Summary
• Over 10 years, CALIPSO and CloudSat have characterized the
current state and interannual variability of clouds
• Active profiling is required to observe critical cloud processes
• Lidar and radar have the accuracy and stability needed to
monitor cloud changes on multi-decadal scales
• With a longer record, can observe their response to climate
warming