Evaluation of the Temperature Trend and Climate Forcing in the Pre- and Post Periods of Satellite Data Assimilation - Satellite-based Applications on Climate Change - page 49

4.1 Introduction

Many previous studies have revealed that changes in the stratosphere and tropo-
sphere are directly or indirectly associated with solar variability attributed to the
decadal solar cycle (van Loon and Shea 1999 ; van Loon and Labitzke 2000 ;
Balachandran et al. 1999 ; Gleisner and Thejll 2003 ; Haigh et al. 2005 ; Crooks
and Gray 2005 ; Gray et al. 2005 ; Matthes et al. 2006 ; Kodera and Shibata 2006 ;
IPCC 2007 ; Rind et al. 2008 ; Meehl and Arblaster 2009 ; Meehl et al. 2009 and
many others). However, great uncertainty remains concerning the actual atmo-
spheric response and differing conclusions in the various studies (Fr¨hlich and
Lean 1998 ; Willson and Mordvinov 2003 ; Haigh 2003 ; Hood 2004 ; Keckhut et al.
2005 ; Scafetta and West 2005 , 2006 ; Lean 2006 ) and other potential climate
forcings (Hansen et al. 2005 ). In addition to solar forcing, other factors such as
the QBO, El Nino Southern Oscillation, and stratospheric aerosol optical depth may
impact the temperature fields.
The observational uncertainties are associated with the different data sources
and other potential climate forcings. First, due to various combinations of
the observation record used to represent the solar variability (Willson and
Mordvinov 2003 ; Fr¨hlich and Lean 2004 ; Dewitte et al. 2005 ), there is disagree-
ment over the solar forcing results (Scafetta and West 2006 ;Lean 2006 ). Based on
the total solar irradiance (TSI) composite of the PMOD between solar cycles 21-23,
Lean ( 2006 ) pointed out the solar contribution to global warming would be negli-
gible. However, Scafetta and West ( 2006 ) relied on the ACRIM TSI composite
concluding that the Sun contributed at least 10-30% of the 0.40

0.04 K global
surface warming. Second, the different atmospheric datasets and techniques used to
compile and integrate the atmospheric data lead to results with different
characteristics that have been questioned. The main atmospheric datasets used
for current community studies include conventional surface and rawinsonde
observations along with rocketsonde data (Dunkerton et al. 1998 ), lidar data
(Keckhut et al. 2005 ), satellite data from the Stratospheric Sounding Unit (SSU)
and Microwave Sounding Unit (MSU) instruments (Scaife et al. 2000 ; Keckhut
et al. 2001 ; Hood 2004 ; Gray et al. 2009 ), and model assimilated datasets (ERA-40
and NCEP/NCAR reanalysis). We note that both assimilated datasets include the
SSU/MSU assimilated observations since November 1978. Also, we note that the
NCEP/NCAR reanalysis assimilated derived temperatures from the satellite data
(Kalnay et al. 1996 ), while the ERA-40 reanalysis assimilated the satellite-
measured radiance data directly (Uppala et al. 2005 ). These differences in approach
could affect trend analyses. The differences in the reanalysis model physics could
generate dynamic differences in the trends and anomaly comparisons (Mo et al.
1995 ). In addition to diverse data sources in the reanalyses, varying length data
source periods can contribute to even more differences. Previous studies have
employed a variety of data sources and observational periods (Pawson and Fiorino
1999 ; van Loon and Shea 2000 ; Labitzke et al. 2002 ; Haigh 2003 ; Keckhut et al.
2005 ; Crooks and Gray 2005 ; Xu and Powell 2010 ) that may have impacted their