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This simple experiment illustrates that the direct radiative effect of rising greenhouse gas
concentrations is muting current trends in global P but that the influences may be difficult to
isolate from natural variability in the present-day climate. We now examine in more detail
how well simulations can capture current changes in the global hydrological cycle.
2.3 Current Changes in the Global Atmospheric Water Cycle
Observationally based estimates of global precipitation changes in Fig.
2
a from the Global
Precipitation Climatology Project (GPCP), which combines infrared and microwave
satellite data over the ocean with infrared and rain-gauge observations over land (Huffman
et al.
2009
), show substantial variability but little discernible trend, consistent with the
simple model results described in Fig.
1
. Current changes in the global atmospheric water
cycle are now examined in more detail.
Figure
4
a-b shows that, on interannual timescales, global mean column integrated water
vapor (W) is strongly coupled with T both in model simulations (here AMIP5 simulations
with specified observed SST and sea ice) and in observations (combined microwave satellite
data
3
over ocean between 50S-50N and ERA Interim reanalysis simulations
4
elsewhere)
and also from the HadCRUH surface specific humidity (q) dataset (Willett et al.
2008
).
Table
3
shows that W rises at *7 %/K when considering interannual variability over the
period 1988-2008, similar to the Clausius-Clapeyron rate as previously demonstrated
(Wentz and Schabel
2000
; Willett et al.
2008
). Correlation between SSM/I-ERA Interim
W and independent HadCRUH q over the period 1989-2003 is remarkable (r = 0.86).
Reanalyses remain unable to adequately simulate global changes in the hydrological
cycle (e.g., Trenberth et al.
2011
; John et al.
2009
) as illustrated by the changes in W and P
in Fig.
4
b-c. This primarily relates to changes in the observing system over the ocean (Dee
et al.
2011
). Nevertheless, changes in T globally and W over land are thought to be
reasonable (Simmons et al.
2010
) as are the simulated interannual changes in top of
atmosphere net radiation (Loeb et al.
2012
) and within the atmosphere as depicted in
Fig.
4
d. The satellite-based ISCCP (Zhang et al.
2004
) and Surface Radiation Budget (SRB)
(Stackhouse Jr. et al.
2011
) estimates of Q
atm
suffer from inaccurate changes in surface
fluxes, for example clear-sky surface longwave fluxes over land.
While coupled model simulations in Fig.
2
a appear to underestimate the observed
GPCP variability, this is not the case for AMIP simulations in Fig.
4
c. There is good
agreement for the positive interannual dP/dT relationship between GPCP and AMIP5 over
the period 1988-2008 (Table
3
) although the robust nature of changes in precipitation
from satellite data over the ocean remains questionable prior to 1995 (Liu et al.
2012
). It is
nevertheless encouraging that a global relationship between Q
atm
from ERA Interim and
P from GPCP emerges, broadly consistent with model simulations and close to unity
(Table
3
) as anticipated from physical grounds (O'Gorman et al.
2012
).
3
We use Scanning Multi-channel Microwave Radiometer (SMMR) and the Special Sensor Microwave
Imager (SSM/I) data that provide ocean retrievals of W and also ocean retrievals of P from the SSM/I
instruments on the F08/F11/F13 series of Defense Meteorological Satellite Program platforms (e.g., Wentz
and Schabel
2000
; John et al.
2009
).
4
Reanalyses combine weather forecast model simulations with available observations using data assimi-
lation to provide a 3-dimensional depiction of atmospheric properties, usually every 6 hours. The European
Centre for Medium-range Weather Forecasts (ECMWF) Interim reanalysis, ERA Interim, is a state of the art
reanalysis system covering the period 1979 to present, which for example assimilates vertical temperature
and humidity information from satellite and conventional observations. A detailed description is given by
Dee et al. (
2011
).
