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Evaluation of Precipitation-Surface Temperature
Relationship in IPCC CMIP5 Models
Renguang Wu
Institute of Space and Earth Information Science
The Chinese University of Hong Kong
Conference on the East Asia and Western Pacific Meteorology and
Climate cum Hong Kong Meteorological Society 25th Anniversary
2-4 November 2013, Hong Kong, China
Co-authors: Jiepeng Chen, Zhiping Wen
Why talk about the precipitation-
surface temperature relationship?
• Precipitation (P) and temperature (T) are two
basic quantities directly related to our lives.
• The P-T relationship can help to understand the
physical processes connecting the atmosphere
with ocean and land.
2
Types of P-T correlation
• Positive P-T correlation (ocean): ocean forcing the atmosphere
The P and T variations are related in different ways that
indicate different physical connections between them:
SST warmer
Ta higher
Ps lower
Lower-level convergence
Ascending motion
More precipitation
3
Types of P-T correlation
• Negative P-T correlation (land): atmosphere influencing land
The P and T variations are related in different ways that
indicate different physical connections between them:
More precipitation
(more clouds)
atmosphere
absorbs/reflects more
shortwave radiationless shortwave radiation reaching the surface
lower surface temperature 4
Types of P-T correlation
• Positive P-T correlation (land/winter):
temperature controlling precipitation
The P and T variations are related in different ways that
indicate different physical connections between them:
less moisture in the atmosphere
Lower surface temperature
less precipitation
5
lower air temperature
Why talk about the precipitation-
surface temperature relationship?
• The P-T relationship can help to understand
the physical processes connecting the
atmosphere with ocean and land.
• The performance of the climate models in
the P-T relationship provides information
about whether the physical processes in the
models are realistic, which may be of great
help for understanding the biases that may
appear in P and T variations.
6
Seasonality of P-SST relationship
P-SST correlation P-∂SST/∂t correlationDJF
JJA
Wu & Kirtman 2007
Datasets
• Precipitation (ocean): GPCP V2, 2.5°x2.5° grid,
1979-2010.
• Precipitation and surface air temperature (land):
University of Delaware, 0.5°×0.5°, 1901-2008.
• Sea surface temperature (SST): NOAA Extended
Reconstruction SST V3, 2.0°x2.0°, 1854 to present.
• Precipitation, surface air temperature, and
surface skin temperature: IPCC CMIP5, 17 models.
Focus on year-to-year variations
• P’ = P – Pm
• T’ = T – Tm
• Pm and Tm are multiple-year mean climatology.
• The correlation between P’ and T’ is calculated
for summer months (MJJAS) and winter
months (NDJFM) separately.
Positive P-T correlationNegative P-T correlation
10
Positive P-T correlationNegative P-T correlation
11
Figure 1 Point-wise correlation between observed monthly mean anomalies of
precipitation and surface temperature (surface air temperature over the land and SST
over the ocean) for groups of months of MJJAS (a) and NDJFM (b) during 1979-2005.
• A positive P-T correlation
in the equatorial central-
eastern Pacific Ocean
through the year.
• The P-T correlation in the
tropical western Pacific
warm pool depends on the
season: positive in boreal
winter and negative in
boreal summer.
• Negative P-T correlation
prevails over the
continental land in
summer, whereas positive
P-T correlation is observed
in high-latitude land
regions in winter.
12
Schematic summary of CMIP 5 long-term experiments with tier 1 and tier 2 experiments
organized around a central core. Green font indicates simulations to be performed only by
models with carbon cycle representations. Experiments in the upper hemisphere are
suitable either for comparison with observations or provide projections, whereas those in
the lower hemisphere are either idealized or diagnostic in nature and aim to provide better
understanding of the climate system and model behavior. (Taylor et al. 2012)
This talk
Table 1 Information of the 17 climate models used in the present analysis.
Institute Model Version Resolution
Grid numbers: lon*lat
BCC BCC-CSM1.1 1 128*64
BNU BNU-ESM 20120504 128*64
CCCMA CanCM4 20120207 128*64
NCAR CCSM4 20120213 288*192
CNRM-
CERFACS
CNRM-CM5 20110701 256*128
CSIRO-QCCCE CSIRO-Mk3.6.0 20120318 192*96
LASG-IAP FGOALS-S2.0 1 128*108
NOAA-GFDL GFDL-CM3 20120227 144*90
NASA-GISS GISS-E2-R 20120205 144*90
NIMR-KMA HadGEM2-AO 20120503 192*145
MOHC HadGEM2-CC 20110927 192*145
INM INM-CM4 20111201 180*120
IPSL IPSL-CM5A-LR 20110406 96*96
MIROC MIROC4h 20110729 640*320
MPI-M MPI-ESM-P 20120315 192*96
MRI MRI-CGCM3 20110831 320*160
NCC NorESM1-ME 20120402 144*96
Institute Acronyms
T85(~1.40625°×1.40625°)
C48(2°×2.5°)
1.875°×1.25°
2°×1.5°
T63(~1.875°×1.875°)
T42(~2.8125°×2.8125°)
NCC NorESM1-ME 20120402 144*96
Institute Acronyms
BCC: Beijing Climate Center, China Meteorological Administration
BNU: College of Global Change and Earth System Science, Beijing Normal
University
CCCMA: Canadian Centre for Climate Modeling and Analysis
NCAR: National Center for Atmospheric Research
CNRM-CERFACS: Centre National de Recherches Meteorologiques/Centre
Europeen de Recherche et Formation Avancees en Calcul Scientifique
CSIRO-QCCCE: Commonwealth Scientific and Industrial Research Organization in
collaboration with Queensland Climate Change Centre of Excellence
LASG-IAP: LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences
NOAA-GFDL: NOAA Geophysical Fluid Dynamics Laboratory
NASA-GISS: NASA Goddard Institute for Space Studies
NIMR-KMA: National Institute of Meteorological Research/Korea Meteorological
Administration
MOHC: Met Office Hadley Centre
INM: Institute for Numerical Mathematics
IPSL: Institut Pierre-Simon Laplace
MIROC: Atmosphere and Ocean Research Institute (The University of Tokyo),
National Institute for Environmental Studies, and Japan Agency for Marine-Earth
Science and Technology
MPI-M: Max Planck Institute for Meteorology
MRI: Meteorological Research Institute
NCC: Norwegian Climate Centre
Taylor diagramTaylor diagram (Taylor 2001) provides a way of quantifying
how well model simulated fields match an observed climate field based on three non-dimensional statistics:
• the ratio of the variances of the two fields: r2 = σ2mod/σ 2obs
(the relative amplitude)
• the correlation between the two fields (R) (the pattern similarity)
• the root-mean-square difference between the two fields (E, which is normalized by the standard deviation of the observed field).
These three statistical quantities are related as follows:
E'2 = E2 - E02 = 1 + r2-2rR (already normalized)
where E0 is the difference in mean, which is often considered separately.
Taylor diagram
The observed field is represented by a point at unit distance from the origin along the
abscissa. All other points, which represent simulated fields, are positioned such that r is the
radial distance from the origin, R is the cosine of the azimuthal angle, and E' is the distance
to the observed point. When the distance to the point representing the observed field is
relatively short, good agreement is found between the simulated and observed fields. In the
limit of perfect agreement, E' would approach zero, and r and R would approach unity.
Taylor diagram displaying
statistical comparisons of
12 model runs’ estimates
with observation of the
West African mean
precipitation pattern for
May to October 2003–
2006. (Xue et al. 2010)
E’
1
r
cos-1R
E'2 = 1 + r2-2rR
The law of cosine
cor lineratio arc
dif arc
Figure 2 The Taylor diagram over the global domain for 17 climate model simulations
compared to the observations during 1979-2005.
• The pattern correlation is better in NDJFM than in MJJAS.
• In both MJJAS & NDJFM, the P-T correlation shows a larger spatial
variability in models than in observations by about 50-60%.
• The BNU has the largest pattern correlation in NDJFM
• The INM has the lowest pattern correlation in both MJJAS & NDJFM.
18
Figure 3 The same as Fig. 2 except for the land.
• The pattern correlation is higher in NDJFM than in MJJAS.
• The pattern correlation tends to be larger when the STD is higher
in MJJAS.
• The INM has the lowest pattern correlation, deviating largely
from the other models.
19
Figure 4 The same as Fig. 2 except for the ocean.
• Compared to the land, the pattern correlation is smaller and has a larger spread
among the models, and the spatial variability of the correlation is smaller.
• The seasonal dependence of the model performance is not as obvious as over
the land.
• Similar to the land in MJJAS, there is a tendency that a larger pattern correlation
corresponds to a higher STD in both MJJAS and NDJFM.
• The BNU performs the best and the MRI model is the worst in both MJJAS and
NDJFM based on the pattern correlation.
20
Figure 5 The same as Fig. 2 except for the tropics (30°S-30°N).
• Most models have a better performance in NDJFM than in
MJJAS.
• There is a notable spread among the models.
• The INM has a relatively low pattern correlation in NDJFM and
MJJAS; the BNU model has somewhat higher pattern correlation
than the other models in both MJJAS and NDJFM.
21
Figure 6 The same as Fig. 2 except for the mid-latitudes of the Northern Hemisphere
(30°-60°N) (a, b) and the mid-latitudes of the Southern Hemisphere (30°-60°S) (c, d).
NH: MJJAS > NDJFM
SH: NDJFM > MJJAS
winter&summer: NH > SH
22
Figure 7 The same as Fig. 2 except for the high-latitudes of the Northern Hemisphere
(60°-90°N) (a, b) and the high-latitudes of the Southern Hemisphere (60°-90°S) (c, d).
NH: NDJFM > MJJAS
SH: NDJFM > MJJAS
summer: SH >> NH
23
The BNU model captures the pattern well, but overestimates the
magnitude of the correlation.
A single simulation of the BNU model
Signal/noise ratio?
a single simulation of the INM model.
The INM model displays inconsistency from observations over the land.
Problem in Iand surface
component?
a single simulation of the MRI model.
The MRI model shows notable differences from observations in some oceanic regions.
Problem in oceanic processes
and/or atmosphere-ocean
coupling processes?
Summary• The P-T correlation is mostly positive over the tropical oceans and
negative over the mid-latitude lands (observations and models).
• The P-T correlation shows obvious seasonal change over the land:
large negative in summer and weak in winter, positive over the
high-latitude regions of the Northern Hemisphere in boreal winter.
• The model performance is better in NDJFM than in MJJAS except
for the mid-latitude lands of the Northern Hemisphere. The model
performance is generally better over the land than over the ocean.
• The seasonal dependence of the model performance is more
obvious over the land than over the ocean and more pronounced
over the mid- and high-latitudes than over the tropics.
• The INM model has difficulty to capture the P-T correlation over
the land. The MRI model has improper P-T correlation over the
equatorial oceanic regions.27
Thanks!