1. CLIVAR Drought Modeling Experiments with CAM3.5: Interim Report
Alfredo Ruiz-Barradas^, Sumant Nigam^,
Adam Phillips*, Clara Deser*
^University of Maryland
*NCAR
13th Annual CCSM Workshop
Breckenridge, CO
June 17-19, 2008

2. US CLIVAR DROUGHT WORKING GROUP
Objective: The primary objective of this working group is to facilitate progress on the understanding and prediction of long-term (multi-year) drought over North America and other drought-prone regions of the world, including an assessment of the impact of global change on drought processes.
-Propose a working definition of drought and related model predictands of drought: e.g.,multy-year dry periods from annual precipitation, PDSI, etc.
-Coordinate evaluations of existing relevant model simulations: CLIVAR’s DRought In COupled Models Project.
-Suggest new experiments (coupled and uncoupled) designed to address some of the outstanding uncertainties mentioned above: this design initiative.
-Coordinate and encourage the analysis of observational data sets to reveal antecedent linkages of multi-year drought.
-Organize a community workshop to present and discuss the results: October 20-24, 2008 in Lincoln, Nebraska
The idea is for several modeling groups to do identical, somewhat idealized, experiments to address issues of model dependence on the response to SSTs (and the role of soil moisture), and to look in more detail at the physical mechanisms linking the SST changes to drought.

3. Modeling Groups CENTER MODEL CONTACT
-NASA/GSFC,……... NSIPP1…. Siegfried Schubert
-Columbia U/LDEO.. CCM3…… Richard Seager
NOAA/GFDL……… GFDL2.1.. Tom Delworth
NCEP……………… GFS……. Jae Schemm
NCAR……………... CAM3.5… Adam Phillips /
Alfredo Ruiz-Barradas

4. NCAR Participation
Was motivated by the improved simulation of North American hydroclimate in the CAM3 development simulations (January 2007).
Case for NCAR’s participation in CLIVAR drought modeling activity was made in last year’s CVWG meeting (June 2007)
CAM3.5’s hydroclimate was evaluated in October 2007.
CAM3.5 AMIP simulation ( ) was initiated in November 2007, and its drought simulation potential evaluated in February 2008.
Drought integrations commenced in March 2008 and 9 key ones have been completed, thanks to Adam Phillips’ untiring efforts and Clara Deser’s support.
We started out being very behind other centers, but are now caught up w.r.t. the core integrations.
When the AMIP simulation was initiated, the Drought Integrations with Columbia’s CCM3 were already (or very close to be) finished.

5. How good is CAM3.5?
CAM3.5’s suitability for investigating droughts was assessed by comparing its Vanilla-AMIP simulation against:
Station precipitation (US-MEX & CRUTS2.1)
Simulations generated by
Model’s previous version (CAM3.0; 1st AMIP ensemble member)
Other CLIVAR Drought Working Group models
CCM3 (run at LDEO; CCM3 goga_new runs atm, 1st ensemble member)
NSIPP (NASA/GSFC; 5th AMIP ensemble member )
NOAA GFS & GFDL (Comparisons pending data access)
Focus on hydroclimate variability, and
SST links

6. Summer Precipitation (JJA, 1950-2000) CI: 1. 0 mm/day (CLIM) CI: 0
Summer Precipitation (JJA, ) CI: 1.0 mm/day (CLIM) CI: 0.3 mm/day (STD)
US-MEX
CAM3.5
vamip
Climatology
CAM3.5 is better than CAM3.0 over central United States, but, perhaps, not elsewhere
CAM3.5 is however more realistic than CCM3 and NSIPP
Standard Deviation
CAM3.5 has a reasonable STD, but not decidedly superior than others
CAM3.0
Box (90º-100ºW, 35º, 45ºN) defines Great Plains Precipitation Index
CCM3
NSIPP

7. Great Plains Precipitation Anomaly Seasonal anomalies smoothed by 12 x (1-2-1 binomial filter)
Full Century ( ) Correlations
[CRU_P, CCM3_P] =0.36 (0.37 detrend)
[CRU_P, CAM3.5_P]=0.32 (0.33 detrend)
[CRU_P, PDSI] =0.82 (0.84 detrend)
Half Century ( ) Correlations
[CRU_P, CCM3_P] =0.58 (0.50 detrend)
[CRU_P, CAM3.5_P]=0.38 (0.19 detrend)
[CRU_P, CAM3.0_P]=0.27 (0.10 detrend)
[CRU_P, NSIPP_P] =0.27 (0.04 detrend)

8. CAM3.5
(vamip)
US-Mex
CAM3.0
CCM3
NSIPP
SST Correlations of the Great Plains Smoothed Summer Precipitation Indices (1-2-1 filter applied once over the summer mean indices)
CAM3.5 correlations are fairly realistic over both the Pacific and Atlantic basins; a definite improvement over CAM3.0
CCM3 correlations are stronger and somewhat more realistic than CAM3.5’s over the Pacific; but not over the Atlantic
NSIPP correlations are less realistic over both basins

9. Conclusions on the assessment of CAM3.5
This first look suggests that CAM3.5 is a competitive model for investigating drought genesis and maintenance.
CAM3.5 is a better model than CAM3.0 (but not CCM3!), in context of Great Plains hydroclimate variability.
CAM3.5’s contribution to CLIVAR’s Drought Modeling activities should be insightful.
The go ahead with the Drought Integrations is given
02/27/2008

10. Can we talk about the drought experiments now?

11. SST forcing patterns for the Drought Integrations
Patterns were obtained from REOF analysis of annual-mean SST anomalies (Schubert et al.)
Linear Trend Pattern (LT)
Pacific Pattern (Pac)
Observational record tells …
Multi-year dry patterns are linked with SST variability (in the absence of other mechanisms, such as soil moisture memory).
Atlantic Pattern (Atl)

12. Experiment Design ● Control integration ● Drought integrations
A 51-year CAM3.5 integration with monthly SST climatology
● Drought integrations
Superpose each SST anomaly pattern on the monthly SST climatology
The SST anomaly pattern itself is seasonally invariant
Each integration is 51 years long (1 year for spin up)
9 integrations have been completed so far
Drought Working Group recommends many more integrations

13. The 9 CAM3.5 Drought Experiments
Focus on North American Droughts
Pac
Atl
Atl cc cn cw
cold nc nw
neutral wc wn ww
warm
wLT
cTrPac
wTrAtl
Pac nn
SST forcing patterns (shown in warm phase)
3/7/2008

14. = +
PcAn - PnAn
PnAw - PnAn
PcAw - PnAn
PcAn-PnAn + PnAw-PnAn
But, do simulated precipitation anomalies look like any historical drought? NEXT…
Both, a cold Pacific and a warm Atlantic induce drought conditions over central US with distinct structure and seasonality.
Nonlinearities accentuate the drought in Spring and weaken it in the other seasons, but in any case, the combined response to both basins is quasi-linear.
c.i.=0.2mm/day 14

15. Can we identify in the real world such drought structure?

16. Dust Bowl Mean Seasonal Precipitation Anomalies for the 1930-39
PcAw - PnAn
Observed precipitation anomalies:
Appear over southeastern US in Spring.
Propagate toward
central US in Summer.
Weaken in Fall.
Climatological conditions are reached over central US in winter.
Evolution and structure
of the simulated drought are quasi-realistic.
Dust Bowl
Mean
Seasonal
Precipitation
Anomalies
for the
period.
Do simulated precipitation anomalies look like any historical drought? Yeah, they do.
Now, what is the tropical contribution? NEXT…
c.i.=0.2mm/day

17. SST seasonal anomalies during the Dust Bowl period (Guan et al. 2008)
Spring
Summer
Features to note:
●seasonality of the SST anomalies in both basins, and
● the tropical structure in both basins during summer.

18. TPcAn- PnAn PnTAw- PnAn
The tropical component of the Pacific and Atlantic SST anomalies induces much of the drought conditions simulated with the whole domain in all seasons.
c.i.=0.2mm/day

19. Water Balance Components in Summer
PcAn - PnAn
PnAw - PnAn
●Precipitation deficit, is largely in
balance with evaporation in both
Integrations. .
●Deficit in precipitation westward
of the Rockies is intensified by the
reduced vertically integrated
moisture flux convergence,
particularly in the case of the cold
Pacific.
c.i.=0.2mm/day

20. What about Interannual variability?
How do other models seem to be doing over the Great Plains?

21. Interannual Variability in Drought Integrations
The Great Plains Precipitation Index
CAM3.5
Seasonal anomalies w.r.t. the model’s own control climatology are plotted after 12 X (1-2-1) smoothing
Control: PnAn
Cold Pacific: PcAn
Warm Atlantic: PnAw
CCM3
CAM3.5 & NSIPP produce multi-year drought even in control simulations (PnAn)
Interannual variability in CCM3 is relatively muted
Cold Pacific (PcAn) and Warm Atlantic (PnAw) can generate normal hydroclimate conditions
Cold Pacific is more influential in all 3 models
NSIPP

22. Do we have an observational target for those idealized simulations?

23. Summer Regressions of the Pacific and Atlantic RPCs (1958-2001)
Cold Pacific RPC
Warm Atlantic RPC
CI=0.1K
Precipitation deficit, is largely in
balance with a reduction in moisture flux convergence over the (northern) Great Plains in both integrations.
CI=0.2mm/day

24. Concluding Remarks
NCAR is an active participant in the CLIVAR sponsored drought modeling activity, well positioned to provide insights
The Cold Pacific and Warm Atlantic experiments indicate a significant role of SSTs in generating droughts over the central US, with tropical Pacific SSTs being quite influential.
Basin influences are generally additive, except in Spring.
Droughts resulting from a cold Pacific and warm Atlantic resemble Dust Bowl conditions, especially in summer.
Atmospheric water-balance analysis indicates a large role for the land surface (i.e., evaporation), likely due to the forcing of drought runs by perpetual SST anomalies.
Interannual variability in model simulations is large, leading to both multi-year droughts in control simulations and normal periods in drought simulations.

25. /ASPHILLI/csm/cam3_3_17_t85_dwg*
Thanks
Data availability:
At NCAR MSS:
/ASPHILLI/csm/cam3_3_17_t85_dwg*
At U. of Maryland:

26. Highest Priority: impact of the leading three patterns (Pac, Atl, LT)
Vanilla-style AMIP experiments
-prescribe each pattern on top of seasonally varying SST climatology
- each run should be at least 51 years (first year is spin-up)
-need a 50+ year control with climatological SST
1) Pac and Atl patterns
a) All combinations of patterns
(8 X 50 years =400 years of simulation)
2) Runs involving the LT pattern
a) +/- LT pattern
b) +/- LT added to (Pac- and Atl+)
c) +/- LT added to (Pac+ and Atl-)
(6 X 50 years = 300 years of simulation)
3) Tropical part of Pac and Atl pattern
a) Tropical only +/-Pac and +/- Atl pattern
(4X50 years =200 years of simulation)
4) Uniform SST warming pattern that has the same global mean SST as + LT(0.16° added to climatology)
(1X50 years =50 years of simulation)
20 runs=1000 years of simulations
plus
soil moisture –related simulations

27. of the Great Plains Precipitation Index Warm-season Regressions
CAM3
3_fv_cmt2_dilute
CAM3
ens01
CAM3.5
NARR
0.91
0.13
0.76
0.79
0.78
0.05
Precipitation
0.84
0.74
0.91
of the Great Plains Precipitation Index
Warm-season Regressions
Moisture Flux
All, Let me just outline the differences between cam3 (dilute) and this particular cam3.5 run for clarity. *Convection: The same in both (with a minor error correction to the Convective Momentum Transport in 3.5). *Cloud fraction: cam3.5 has the updated cloud-fraction calculation after the pcond prognostic condensate calculation AND the freeze drying of clouds at low humidity values. *Aerosols/GHGs: cam3.0 has the old default aerosols and prescribed greenhouse gases. cam3.5 uses an updated aereosol dataset and GHGs (not CO2) are predicted. *CLM: cam3.5 uses clm3.5 (w/ new surface datasets) Cloud formation tuning values are different also (rhminl/h for those who know!) As Aiguo suggested, I suspect it is the land changes that lead to the simulation differences. For info: The cam3 dilute run: uses cam tag cam3_3_17 (with clm tag - clm3_expa_66) The cam3.5 run: uses cam3_5_08 (with clm tag - pftintdat08_clm3_5_05) Thanks Rich
0.69
0.57
0.13
Evaporation
RED numbers are area-averaged
anomalies over the Great Plains box
0.20
0.45
0.76

28. GPP Indices

29. Mean monthly anomalies of smoothed seasonal Great Plains Indices.
Evaporation anomalies are
Comparable to precipitation anomalies

30. Mean seasonal anomalies of area- averaged Great Plain
PcAn - PnAn
Mean seasonal
anomalies of area-
averaged Great Plain
Indices of P, E, and MFC.
Evaporation anomalies are
comparable to precipitation
Anomalies.
PnAw - PnAn
In Summer:
MFC > P - ET P ET
MFC

31. PcAn - PnAn
PnAw - PnAn
Geopotential Height
at 200 mb
c.i.=5 m