Upload
hayden-lockhart
View
216
Download
0
Tags:
Embed Size (px)
Citation preview
[email protected]© University of Reading 20091
Monitoring and understanding current changes in the global energy & water cycles
Richard Allan
[email protected]© University of Reading 20092
• Increased Precipitation• More Intense Rainfall• More droughts• Wet regions get wetter,
dry regions get drier
Precipitation Change (%)
CLIMATE MODEL PROJECTIONS IPCC WGI
Precipitation Intensity
Dry Days
[email protected]© University of Reading 20093
Scenario
Feedbacks
e.g. Hawkins & Sutton (2009) BAMS
• How will the water cycle respond to warming?• Can we effectively monitor current changes in the
Earth’s energy balance and water cycle?• What information can Earth Observation datasets
provide on cloud feedbacks? Are cloud feedback/water cycle issues linked?
• Can we provide near-real time monitoring of models and observations using satellite data?
[email protected]© University of Reading 20094
Some background
• Joke slide
“Does anyone want to buy my nearly-
new research student?”
[email protected]© University of Reading 20095
Winning my freedom from Met Office…(but only as far as ESSC)
Some background
[email protected]© University of Reading 20096
Winning my freedom from Met Office…(but only as far as ESSC)
Important numbers!
Some background
[email protected]© University of Reading 20097
Low-level water vapour rises with temperature at ~7%/K in models & observations
John et al. (2009) GRL; Allan (2009) J Climate
models
Wat
er V
apou
r (m
m)
[email protected]© University of Reading 20098
1979-2002
For a given precipitation event, more moisture would suggest more intense rainfall
Can realism of model projections be assessed?
[email protected]© University of Reading 20099
Frequency of rainfall intensities vary with
SST in models and obs
• Frequency of intense rainfall increases with warming in models and satellite data
• Model scaling close to 7%/K expected from Clausius Clapeyron
• SSM/I satellite data suggest a greater response of intense rainfall to warming
dP/dSST=7%/K
Allan and Soden (2008) Science
[email protected]© University of Reading 200910 Trenberth et al. (2009) BAMS
[email protected]© University of Reading 200911
Models simulate robust response of clear-sky radiation to warming (~2-3 Wm-2K-1) and a resulting
increase in precipitation to balance (~3 %K-1) e.g. Allen and Ingram (2002) Nature, Stephens & Ellis (2008) J. Clim
Rad
iativ
e co
olin
g, c
lear
(W
m-2K
-1)
Allan (2009) J Clim
[email protected]© University of Reading 200912
Contrasting precipitation response expected
Pre
cipi
tatio
n Heavy rain follows moisture (~7%/K)
Mean Precipitation linked to
radiation balance (~3%/K)
Light Precipitation (-?%/K)
Temperature
e.g.Held & Soden (2006) J. Clim; Trenberth et al. (2003) BAMS; Allen & Ingram (2002) Nature
[email protected]© University of Reading 200913
Contrasting precipitation response in ascending and descending portions of the tropical circulation
GPCP/NCEP Models
ascent
descent
Allan and Soden (2007) GRL
Pre
cipi
tatio
n ch
ange
(m
m/d
ay)
[email protected]© University of Reading 200914
Future Plans
[email protected]© University of Reading 200915
- NERC PREPARE project (Met Office; ETH Zurich)
- HadIR/JCRP projects (Met Office, NCEO)
- leading ERL special focus issue (with Beate Liepert)
- Planned UK/Danish Met Services NERC partnership grant on GPS ; NERC Changing Water Cycle program; NCEO; Royal Society
- Changes in African and Asian Rainfall (Grimes, Turner, NCAS)
Monitoring and understanding changes in the global energy/water cycles
Pre
cipi
tatio
n A
nom
aly
(mm
/day
)
Radiation
Anom
aly (W
m-2)
[email protected]© University of Reading 200916 Allan et al. (2007) QJRMS
2008
Are the cloud feedback and water/energy cycles issues linked?- Radiative and microphysical properties of marine stratiform cloud (Stephens, Colorado; ECMWF) and ice cloud (Hogan)- CloudSat/CALIPSO, GERB/CERES, SSM/I (NCEO, Imperial, NASA)- Surface and Atmospheric Radiation Budget and aerosol (NASA, ETH)
[email protected]© University of Reading 200917
Continuous Monitoring of models and observations
Example 1:
Global water cycle and Earth’s energy balance
Essential Climate variables (ESA Harwell, NCEO)
Reanalyses for climate (ECMWF)
[email protected]© University of Reading 200918
Continuous Monitoring of models and observationsExample 2: Model development with Met Office/NCAS from NWP (below,
Milton, Brooks) to climate (Ringer, Williams) via Cascade (Woolnough)
13th March | 14th March 2006
Model S
W albedo
2005 2006
Change in model minus GERB flux differences: relate to change in model physics implementation
Identify problem and fix: convective cloud decay time-scale
Monitor improvement using GERB/CloudSat
1 2
3
Allan et al. (2007) QJRMS
[email protected]© University of Reading 200919Courtesy of Jim Haywood
Continuous Monitoring of models and observationsExample 3: field campaigns (e.g. RADAGAST; GERBILS; FENNEC) and opportunistic case studies…
Met Office NAME modelNOAA17 satellite image 20 March 2009 10:06
[email protected]© University of Reading 200920Courtesy of Jim Haywood
[email protected]© University of Reading 200921Courtesy of Jim Haywood
[email protected]© University of Reading 200922Courtesy of Jim Haywood
[email protected]© University of Reading 200923Courtesy of Jim Haywood
[email protected]© University of Reading 200924Courtesy of Jim Haywood
[email protected]© University of Reading 200925Courtesy of Jim Haywood
[email protected]© University of Reading 200926
Using GERB/SEVIRI to quantify radiative effects of persistent contrail cirrus
[email protected]© University of Reading 200927
Conclusions• Radiative energy and water cycles
– fundamentally linked– crucial for climate impacts
• Combining observations with models and a robust physical basis is essential for– understanding current changes in climate– quantifying and assessing feedbacks operating– improving confidence in predictions
• Continuous monitoring of observations and model simulations enable us to– track current trajectory of climate change– detect surprises in the climate system– link and develop seamless prediction systems