Hervé Douville Météo-France/CNRM [email protected] Acknowledgements: B. Decharme, R. Alkama
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Transcript of Hervé Douville Météo-France/CNRM [email protected] Acknowledgements: B. Decharme, R. Alkama
Hervé DouvilleMétéo-France/CNRM
Acknowledgements:
B. Decharme, R. Alkama
and Y. Peings
WCRP Seasonal Prediction Workshop, Exeter, 1-3 December 2010
Land surface contributionto climate variability
and predictability
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Outlines
Background and motivations
1. Land surface data and statistical studies• Global land surface products
• Data intercomparison and model evaluation
• Statistical evidence of predictable land surface impacts
2. Numerical sensitivity experiments• Pioneering studies
• Numerical evidence of local land surface impacts
• Numerical evidence of remote land surface impacts
Conclusions, prospects and issues
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Seasonal prediction:A question of remote control ?
The forecast
The land surface component
The AOGCM
The anthropogenic radiative component
The stratospheric component
A « slave » component ?
« Need to improve the representation of climate system interactions and their potential to improve forecast quality. » (WCRP position paper, Barcelona 2007)
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GLACE: Global Land-Atmosphere Coupling Experiment (a GEWEX & CLIVAR initiative)
?
GLACE-1 multi-model land-atmosphere coupling strength based on the reproductibility of 5-day precipitation (Koster et al. 2006). Not sufficient to evaluate the impact of land state initialization on seasonal forecast skill => GLACE-2
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Relevance of land-atmosphere coupling for climate predictability: At least 3 conditions
1. Land surface anomalies must have sizeable (i.e. potential predictability) and realistic (i.e. effective predictability) impacts on atmospheric variability
2. Land surface anomalies must be predictable at the selected timescale (using dynamical and/or statistical tools)
3. Real-time global land surface analyses must be available for initializing the relevant land surface variables (soil moisture, snow mass, …)
NB: focus on monthly to seasonal timescale only.
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(lack of) Land surface dataAnd statistical studies
Global (satellite) land surface observationso Snow: visible (since 1967), passive micro. (SMMR since 1978, …)o Soil moisture: passive & active micro. (AMSR since 2002, ASCAT, …)o Total water storage variations: gravimetry (GRACE since 2002)
Off-line land surface model simulationso GSWP-2 (1986-1995): 13 models driven with ISLSCP2 forcing data o GLDAS (1979-present): 4 models driven with bias-corrected reanalyses
or NOAA/GDAS real-time analyses (since 2000)o VIC (Sheffield and Wood 2008) or ISBA (Alkama et al. 2010) driven
with 1950-2006 Princeton Univ. (Sheffield et al. 2006)
On-line LDAS systemso Soil moisture analysis based on the assimilation of screen-level
temperature and humidity (e.g. Météo-France, ECMWF, Met Office, …)o Assimilation of NESDIS snow extent (e.g. ECMWF since 2004)o Assimilation of ASCAT soil moisture (e.g. Met Office since July 2010)
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LSM
ISLSCP-2 (1986-1995), Princeton Univ. (1950-2006), …
3-hourly atmospheric forcing Fixed or monthly physiography
Soil moisture & snow mass climatologyEvaporationRunoff
RRM
Discharge In Situ Observ.
AGCM
T2m et P
Off-line land surface simulations
Satellite Data
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Land surface data intercomparisonEx: Central Europe
• ISBA driven by Princeton University atm. forcings (1950-2006)
• ERA-Interim (1989-2010)
• ERA40 (1958-2001)
• GSWP multi-model driven by ISLSCP2 atm. forcings (1986-1995)
vs 1989-1995 climatology
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Land surface model evaluationISBA-TRIP vs GRACE and GRDC data
ISBA = soil moisture + snow + river
Monthly water storage variation (kg/m²/day) anomalies and mean annual cycle
Alkama et al., J. Hydromet., 2010
Monthly river discharge (kg/m²/day) anomalies and mean annual cycle
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Statistical evidenceof land surface contribution to predictability
North American summer temperature (e.g. Alfaro et al. 2006) and precipitation (e.g. Quiring and Kluver 2009)
Sahelian summer monsoon precipitation (e.g. Philippon and Fontaine 2002, Douville et al. 2007)
Indian summer monsoon precipitation (e.g. Blanford 1884, Fasullo 2004, Peings and Douville 2009)
Winter North Atlantic Oscillation (e.g. Cohen and Entekhabi 1999, Hardiman et al. 2008, Cohen et al. 2010)
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Statistical evidence:North America T2m & P
Maps of JJA Tmax prediction skill (cross-validation over 1950-2001)
using May Pacific SST and/or PDSI (soil moisture proxy) predictors.
Alfaro et al. 2006
Northern Great Plains heavy & light AM snowfall composites (1929-1999) with interquartile range.Quiring and Kluver 2009
T2m (°C)
Cum. P (mm)
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Statistical evidence:West African summer monsoon P
• Hypothesis: 2nd rainy season over the Guinean Coast affects subsequent summer monsoon rainfall over the Sahel through a soil moisture memory effect (Landsea et al. 1993, Philippon and Fontaine 2002)• But: Stochastic artefact mediated through tropical SST and partly due to multi-decadal variability ? (Douville et al. 2007)
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Statistical evidence:Indian summer monsoon P
• Hypothesis: Winter and spring Eurasian snow cover affects subsequent summer monsoon rainfall over India (Blanford 1884, Fasullo 2004)• But: Such a statistical relationship is neither robust nor stationary in the instrumental record and is not captured by CMIP3 historical simulations (Peings and Douville 2009)
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Statistical evidence:Wintertime N.H. extratropical variability
• Hypothesis: Fall (i.e. October) snow cover over Siberia affects subsequent winter NAO (Cohen and Entekhabi 1999)• But: Not found in CMIP3 models (Hardiman et al. 2008) though the observed relationship is robust and was verified in winter 2009-2010 (Cohen et al. 2010)
JFM 2010forecasted vs observed temperature anomalies
(Cohen et al. 2010)A negative AO/NAO winter preceded by
above normal Siberian snow cover
SnowCast Observations
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Further evidence based onnumerical sensitivity experiments
Pionneering studies: Land vs SST impact on precipitation variability (e.g. Koster et al. 2000), dynamical vs non-dynamical feedback (e.g. Douville et al. 2001)
GLACE-2 and related studies (e.g. Douville 2009, Koster et al. 2010, Peings et al. 2010)
Remote impacts of Eurasian snow cover (e.g. Fletcher et al. 2009, Peings et al. in preparation)
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2ALO / 2
AO
Control experiment ALOA: Atmosphere onlyL: Interactive Land HydrologyO: Observed instead of climatol. monthly mean SST
Varia
nce
of
an
nual
pre
cipita
tion
Impact of Land vs SST variability on annual mean precipitation (Koster et al. 2000)
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Sahel
dry wet dry wetdry wet
SouthAsia
dry wet dry wetdry wet
PEAnom. P-E
Dynamical (P-E) versus non-dynamical (E) soil moisture feedbacks (Douville et al. 2001)
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ControlNo nudgingObs. SST
NudgingObs. SST
NudgingClim. SST
Zonal mean annual cycle of: Stdev Pot. Pred. (ANOVA) Skill (ACC)
SST vs land surface impacts on monthly T2m predictability over land (Douville 2009)
75°N
55°S
75°N
55°S
75°N
55°S
19 19
16-30 days
31-45 days
46-60 days
temperature precipitation
GLACE-2 coordinated experiments“Consensus” skill due to land initialization
2-months hindcasts initialized on 1st & 15th June, July and August => 6 hindcasts x 10 years (1986-1995) x 10 members = 600 runs.
13 models (“weaker” models are averaged in with “stronger” ones).
Conditional skill show stronger increase.
Koster et al., GRL, 2010
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Impact of snow boundary / initial conditions on springtime (MAM) T2m (Peings et al. 2010)
Total Stdev Pot. Predictability Skill3 ensemble experiments:
Control (CTL)Interactive snow cover
SBC – CTLImpact of
snow relaxation
SIC – CTLImpact of
snow initialization
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Remote impact of Siberian snow coveron DJF NAO (Fletcher et al. 2009)
A snow-NAO relationship through a
stratospheric pathway
2 pairs of 100-member
ensemble experiments:
High minus Low fall snow cover
over Siberia
a) SWnet (d1-d15) b) MSLP (d24-d50)
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Remote impact of Siberian snow coveron DJF NAO (Peings et al. 2011)
DSS* - CTL*Improved polar
vortex climatology through equatorial
stratospheric nudging
2 pairs of 50-member ensemble
experiments:
DSS - CTLDeep Snow over
Siberia
MSLP (hPa) Zonal mean Z (m)
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CONCLUSIONS
Growing statistical and numerical evidence of both local and remote impacts of land surface initial conditions on climate predictability (though some of these studies are questionnable);
Such impacts are highly model-dependent, variable across regions and seasons, and sensitive to the magnitude of the land surface anomalies;
Long-range predictability of the land surface hydrology seems limited (mainly by the low predictability of precipitation) but needs further evaluation (i.e. new observations and data assimilation systems);
Land surface impacts do not amount to simple changes in the surface energy budget, but also involve large-scale dynamical and cloud feedbacks;
Land surface contribution to climate predictability should not be neglected given the weak SST impact on extratropical predictability.
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Observations: SMOS (L-band, 2010) & SMAP (Soil Moisture Active and Passive, 2015) for upper soil moisture, improved use of passive microwave data for snow (until ESA’s CoReH20 mission), GRACE for total water storage variations, SWOT (Surface Water and Ocean Topography) , …
Land Surface Models & Data Assimilation Systems: increased vertical discretization, simulation of water bodies including floodplains, improved representation of snow under canopy (e.g. SnowMIP), multi-spectral surface albedo and related data assimilation(MSG, MODIS), off-line model inter-comparison without (GSWP-3?) and with (PILDAS?) data assimilation, global & multi-decadal (at least since 1989) surface reanalysis, …
Sensitivity experiments: SCM studies, follow-on of GLACE-2 looking at soil moisture but also snow water equivalent and possibly surface albedo, GLACE-type versus state-of-the-art (rather than random) initialization, coupled vs AMIP-type experiments, process-oriented case studies, statistical adaptation of dynamical forecasts using land surface variables, …
PROSPECTS(OPEN FOR DISCUSSION)
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(CONTROVERSIAL) ISSUES
Statistical benchmarks ACC and RMSS differences
between sCast and DEMETER hindcasts of DJF surface
temperature (72/73 to 92/93) (red / blue means sCast has
greater / lower skill)(Cohen and Fletcher 2007)
What about vegetation ?Difference in statistical
significance of temporal ACCs between two sets of hindcasts of JJA T2m using observed vs
climatological vegetation(red / blue means increased /
decreased significance)(Gao et al. 2008)
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(CONTROVERSIAL) ISSUES
Towards decadal predictions ?
Verification of the first genuine dynamical decadal prediction
by Keenlyside et al. 2008for global mean temperature
(from http://www.realclimate.org) A land surface contribution would
be welcome but is unlikely…
BekeleESM
BoltNWP
Seamless is not questionless…
End