Land Surface Processes in
Global Climate Models (2)
Study water cycle trend using the NCAR
Community Land Model (CLM3)
Question: What is the trend of water cycle in the last 50 years?
Model experiments
 57 years (1948-2004) of CLM offline simulation
 Resolution: T42 (~2.8º)
 The forcing dataset: 3-hrly and T62 (~1.875°)
Precipitation, surface air temperature, downward solar
radiation, specific humidity, wind speed and air
pressure
• Combined the intramonthly variations from the
NCEP/NCAR 6-hrly reanalysis with monthly time
series from station records (T, P, CLD)
 The validation datasets include streamflow from 921
rivers around the world, continental freshwater
discharge, surface runoff and soil moisture
Qian, Dai, Trenberth and Oleson (2006), J. Hydrometeor.
Simulated mean annual cycle for the world’s
10 largest rivers: validation using observation
NCEP Adjusted
NCEP unadjusted
Observation
Amazon
Mississippi
Congo
Yenisey
Orinoco
Parana
Changjiang
Bahmaputra
Lena
Mekong
Simulated long-term mean streamflow for the world’s
200 largest rivers: Validation using observation
Simulated water-year annual mean streamflow
for 12 rivers: validation against observation
Simulated long-term mean runoff:
Validation against Fekete et al. data
Qian, Dai, Trenberth and Oleson (2006), J. Hydrometeor.
Comparison of long-term mean freshwater
discharge into the global oceans
Comparison of soil moisture:
CLM3 vs measurements at Illinois, U.S.
Summary of global simulation using CLM3
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The CLM3 reproduces many aspects of the long-term mean, annual cycle,
interannual and decadal variations, and trends of streamflow for many large
rivers (e.g., the Orinoco, Changjiang, Mississippi, etc.), although substantial
biases exist.
The simulated long-term-mean freshwater discharge into the global and
individual oceans is comparable to 921 river-based observational estimates.
Observed soil moisture variations over Illinois and parts of Eurasia are
generally simulated well, with the dominant influence coming from
precipitation.
It is also shown that unrealistically low intensity and high frequency of
precipitation in original NCEP reanalysis result in too much evaporation and
too little runoff, which leads to lower than observed river flows. This problem
can be reduced by adjusting the precipitation rates using observed
precipitation-frequency maps.
The results suggest that the CLM3 simulations are useful for climate change
analysis.
The evaporation trend in the Mississippi River basin
E participates in two balances at the land surface
Energy balance
Fsw
Water balance
P
Flw LH SH
G
E
dS/dt
R
Previous studies: Incomplete observations lead to conflicting estimates of E trend
(1) Energy balance: Cloud is increasing, Fsw is decreasing, Pan evaporation
(Epan) is decreasing
Peterson et al. (1995, Nature): E is decreasing
Brutsaert & Parlange (1998, Nature) and Golubev et al. (2001, GRL):
E is negatively correlated with Epan, so E is increasing
Roderick and Farquhar (2002, Science): E is negatively correlated with Epan
only under specific conditions, not in general. So E is decreasing
(2) Water balance: P is increasing, R is increasing, P-R is increasing
Milly & Dunne (2001, GRL); Walter et al. (2004, J. Hydrometeor.): E is increasing
Questions:
Has ET increased or decreased?
Can we satisfy BOTH the heat and water budgets?
Our study: Complete evaluation of these two budgets
using observation-constrained global land model
simulations
Qian, Dai, and Trenberth (2007), J. Climate
Basin-averaged energy budget
Basin-averaged water budget
Model sensitivity experiments on factors affecting E
trend: dominated by precipitation change
Trend map of water budget
Precip
Evap
Runoff
Storage
Qian, Dai, and Trenberth (2007), J. Climate
Trend map of energy budget
SW
LH
LW
SH
Summary:
Water cycle trend of Mississippi River basin
Fsw
P
Flw LH SH
G
Energy budget
E
dS/dt
R
Water budget
Both energy balance and water balance support that evaporation is
increasing, and the water cycle is intensified during the last 50 years!