Upload
kerry-chase
View
224
Download
0
Tags:
Embed Size (px)
Citation preview
Global Modeling StatusThomas Lachlan-Cope1 and Keith M. Hines2
1British Antarctic SurveyCambridge, UK
2Polar Meteorology GroupByrd Polar Research Center
The Ohio State University
A Short History of the World (with emphasis on cloud
modeling in GCMs)It had been common for GCMs to parameterize the radiative large-scale clouds. Cloud fraction was based upon factors such as relative humidity, static stability, … (e.g., Slingo-type schemes)
This decade GCMs have moved toward prognostic radiative clouds (can include water and ice clouds).
The radiative clouds are being made more consistent with the precipitating clouds.
More advanced cloud prognostic schemes (e.g., two-moment microphysics) are recently being implemented.
Sea Level Pressure Combination Anomalies > 0.5 Standard Deviations
1871-2006 NCAR CAM3.5 AMIP-type Simulation
An evaluation of Antarctic near-surface temperature and snowfall in IPCC AR4 GCMs
Andrew J. Monaghan, David H. Bromwich, Ryan L. FogtPresented by Keith Hines
Polar Meteorology Group
Byrd Polar Research CenterThe Ohio State University
Columbus, Ohio, USA
David Schneider
NCARBoulder, Colorado, USA
Research funded by NSF-OPP and NASA
Tem perature(C )
-55
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Accum ulation(m m /yr)
0
20
50
100
200
300
400
500
600
700
800
900
1000
1500
2000
Annual mean near-surface temperature(from AMPS Polar MM5)Overlain on RAMP DEM
Annual mean snowfall(from AMPS Polar MM5)Overlain on RAMP DEM
Precip ita tion(m m /yr)
0
20
50
100
200
300
400
500
600
700
800
900
1000
1500
2000
BackgroundBackground
• Due to strong natural multidecadal climate variability in Antarctica, recent work has focused on spatially and temporally extending Antarctic snowfall and temperature records, as well as temporally extending the SAM index.
• These new records allow us to assess current Antarctic climate in a complete, multi-decadal context. Additionally, such reconstructions provide a means of assessing global climate model (GCM) simulations over Antarctica.
• In this presentation, we employ the new extended records to evaluate GCM simulations run in support of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). The observational records used are:
1. A 50-year annual Antarctic snowfall record (Monaghan et al. 2006)2. A ~120-year annual Antarctic near-surface temperature record based on ice core
isotope records (Schneider et al. 2006)3. A 46-year annual and seasonal near-surface temperature record based on
instrumental data (Monaghan et al. in prep)4. A ~140-year annual record of the SAM (Fogt et al., in prep)
• Validations of these datasets indicate that they are robust
Part 1:Part 1:Comparing IPCC AR4 GCMs to Comparing IPCC AR4 GCMs to observed Antarctic climate variabilityobserved Antarctic climate variability
Annual Antarctic near-surface temperatures in Annual Antarctic near-surface temperatures in Five IPCC AR4 GCM Ensembles: 1880-presentFive IPCC AR4 GCM Ensembles: 1880-present
Observed
NASA GISS-ER
MPI-ECHAM5
MRI-CGCM2_3_2a
NCAR-CCSM3_0
Grand Ensemble
Canadian CGCM3_1
Conclusion:Antarctic temperature in GCMs increases at 2-3x observed
Bromwich et al. (in prep)
Annual Antarctic Snowfall in Five IPCC AR4 Annual Antarctic Snowfall in Five IPCC AR4 GCM Ensembles: 1880-presentGCM Ensembles: 1880-present
Observed
NASA GISS-ER
MPI-ECHAM5
MRI-CGCM2_3_2a
NCAR-CCSM3_0
Grand Ensemble
Canadian CGCM3_1
Conclusion:Antarctic snowfall in GCMs increases similarly to observed, but it is uncertain whether the GCMs would simulate a downturn since the late 1990s, as observed
Bromwich et al. (in prep)
Sensitivity of Annual Sensitivity of Annual Antarctic Snowfall to Antarctic Snowfall to Antarctic temperature Antarctic temperature in IPCC AR4 GCMs in IPCC AR4 GCMs
Sensitivity of annual Antarctic snowfall to temperature
0
2
4
6
8
10
12A
NT
_OB
S
S_H
EM
GR
A
CC
C
GIS
MP
I
MR
I
NC
A
Sen
siti
vity
(%
y-1
)
1882-1997
1962-1997
1980-1997
Conclusion:The GCMs have approximately the same sensitivity as observed. Therefore, if GCM Antarctic temperature projections are accurate, it may be expected that snowfall projections will be reasonable.
-0.6
-0.5
-0.4
-0.3
-0.2-0.1
0
0.1
0.2
0.3
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
176
178
180
182
184186
188
190
192
194
ANT_TEMP ANT_ACCUM
Bromwich et al. (in preparation)
(%/K
)
SAM Reconstruction vs. IPCC AR4 SAM, Annual MeanSAM Reconstruction vs. IPCC AR4 SAM, Annual Mean
Gray shaded region corresponds to SAM calculated from 18 IPCC AR4 models, red is the grand ensemble mean
Blue is a reconstruction based on station pressure observations in the Southern Ocean -see Fogt et al. poster at this meeting
Fogt et al. (in preparation)
Conclusion:• Most models have much lower annual SAM values in the
early 20th Century, thereby producing significant long-term trends that are not in the reconstruction.
• Models with and without time variable ozone forcing give different trends over the last 50 years, suggesting that accurate ozone concentrations are need for accurate 20th and 21st Century SAM predictions.
Part 2:Part 2:Why are GCM temperature trends too Why are GCM temperature trends too strong?strong?
Annual Antarctic Temperature vs. the SAMAnnual Antarctic Temperature vs. the SAMObserved and Observed and IPCC AR4 GCMsIPCC AR4 GCMs
CCC
NCAMRIMPI
GIS
OBS
Five-year running means of detrended annual Antarctic near-surface temperature anomalies (K) plotted against detrended annual SAM anomalies (units are standard deviations) for the observations (a; 1962-2003) and the GCMs (b-f; 1882-1997). The observations are based on the Monaghan et al. (in prep) temperature reconstruction versus the Marshall (2003) SAM.
Bromwich et al. (in prep)
Conclusion:The observed sensitivity of Antarctic temperatures to the SAM is strongly negative overall. In the GCMs, the sensitivity is weak.
Annual Antarctic Temperature vs. LW Annual Antarctic Temperature vs. LW Radiation for the IPCC Grand Ensemble Radiation for the IPCC Grand Ensemble
b) T vs. LW Down, Cloudy-sky
c) T vs. Precipitable Water Vapor
a) T vs. LW Down, All-sky
Conclusion:•A LW radiation feedback at the surface is mainly due to an increase in water vapor (not clouds) in the IPCC AR4 GCMs.
•We still are working to figure out why so much moisture is being pumped into the Antarctic Atmosphere in the GCMs
Bromwich et al. (in prep)
Summary: Summary:
•.Antarctic near-surface temperature trends are overestimated by ~2-3x over the 20th century by IPCC AR4 GCMs. Snowfall projections seem reasonable given the data available. The GCMs overestimate 20th century SAM trends. GCMs that include observed stratospheric ozone during the late 20th century do the best at capturing the SAM increase since the 1960s.
•The sensitivity of Antarctic snowfall to regional temperature changes is consistent with GCM estimates of ~5%/K. However, the linkage with the temperature behavior over Antarctica is complex and arises because of changes in the atmospheric circulation.
•Observed Antarctic annual near-surface temperature trends are strongly related to SAM trends. However, in the IPCC AR4 GCMs, the Antarctic temperature sensitivity to the SAM is weak compared to a spurious water vapor feedback that is increasing downwelling longwave radiation in the models. Resolving this issue is of first-order importance in order to provide realistic Antarctic temperature simulations for the 21st century.
What is the point of this?
Well, obviously, Antarctic snowfall is linked to Antarctic clouds. Is the treatment of
precipitation for Antarctic clouds reliable enough in global models to tackle the
important issues for decadal and centennial climate variability and change?
The answer is uncertain.
Are These Mesoscale Modeling Issues also Relevant for GCMs?
• Limited observations for comparison/inspiration/verification• Low aerosol concentrations• Clear-sky precipitation/diamond dust• Thin ice clouds• Ice cloud physics less well understood than liquid cloud physics• Non-spherical ice particles• Are the more frequent Arctic field programs relevant for the
Antarctic?
• How can we make use of more advanced (two-moment) cloud microphysical parameterizations?
• How can we make use of remote sensing?• Synergy with ice core studies
Issues for Mesoscale Modeling of Antarctic Clouds
• Limited observations for comparison/inspiration/verification• Low aerosol concentrations• Clear-sky precipitation/diamond dust• Thin ice clouds• Ice cloud physics less well understood than liquid cloud physics• Non-spherical ice particles• Are the more frequent Arctic field programs relevant for the
Antarctic?• How can we make use of more advanced (two-moment) cloud
microphysical parameterizations?• How can we make use of remote sensing?• Synergy with ice core studies