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. (2009) . Furthermore, regional-mean multidecadal temperatures derived from observation networks may be affected by bias resulting from reorganizations of observation sites in networks. Therefore, multidecadal surface temperature trends could be affected by biases and uncertainties due to nonspatially representative influences ( Klotzbach et al. 2009 and references therein). Thickness is a well-known meteorological quantity, defined by pressures and altitudes at two atmospheric levels and
. (2009) . Furthermore, regional-mean multidecadal temperatures derived from observation networks may be affected by bias resulting from reorganizations of observation sites in networks. Therefore, multidecadal surface temperature trends could be affected by biases and uncertainties due to nonspatially representative influences ( Klotzbach et al. 2009 and references therein). Thickness is a well-known meteorological quantity, defined by pressures and altitudes at two atmospheric levels and
1. Introduction The temperatures measured by surface drifters play a key role in the establishment of bulk sea surface temperature (SST) maps in blended products, as they are used to calibrate or validate satellite retrievals ( Reynolds et al. 2007 ; O’Carroll et al. 2008 ). They also contribute a significant share of all in situ SST data in the last two decades (close to 40% of the data since 2000; Rayner et al. 2009 ). Compared to other in situ SST observations from ships, it appears that
1. Introduction The temperatures measured by surface drifters play a key role in the establishment of bulk sea surface temperature (SST) maps in blended products, as they are used to calibrate or validate satellite retrievals ( Reynolds et al. 2007 ; O’Carroll et al. 2008 ). They also contribute a significant share of all in situ SST data in the last two decades (close to 40% of the data since 2000; Rayner et al. 2009 ). Compared to other in situ SST observations from ships, it appears that
1. Introduction Sea surface temperature (SST) is one of the key parameters for understanding air–sea interaction processes. In the field of global climatology, air–sea interactions at time scales of longer than a day have been thoroughly studied, and the impact of SST on the seasonal or annual climate is now widely accepted (e.g., Bjerknes 1969 ; Horel and Wallace 1981 ). However, in regional meteorology, the importance of air–sea interaction is less recognized. This is because the diurnal
1. Introduction Sea surface temperature (SST) is one of the key parameters for understanding air–sea interaction processes. In the field of global climatology, air–sea interactions at time scales of longer than a day have been thoroughly studied, and the impact of SST on the seasonal or annual climate is now widely accepted (e.g., Bjerknes 1969 ; Horel and Wallace 1981 ). However, in regional meteorology, the importance of air–sea interaction is less recognized. This is because the diurnal
2000 ; Knight et al. 2005 ). In the present paper, we use twentieth-century observations of global surface temperature combined with analyses of coupled general circulation model (GCM) simulations in an attempt to differentiate between the externally forced and natural aspects of the observed temperature trends. Investigators have routinely employed time filtering combined with some procedure to reduce the number of spatial degrees of freedom [typically using empirical orthogonal function (EOF
2000 ; Knight et al. 2005 ). In the present paper, we use twentieth-century observations of global surface temperature combined with analyses of coupled general circulation model (GCM) simulations in an attempt to differentiate between the externally forced and natural aspects of the observed temperature trends. Investigators have routinely employed time filtering combined with some procedure to reduce the number of spatial degrees of freedom [typically using empirical orthogonal function (EOF
-defined parametric statistical tests are applied in order to evaluate the trend-stationary assumption in global sea surface temperature (SST). 2. Methods Parametric statistical tests have been developed in econometrics for discriminating between difference-stationary and trend-stationary time series. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test ( Kwiatkowski et al. 1992 ) tests the null hypothesis of a trend-stationary process against a difference-stationary alternative. Rejection of the null hypothesis
-defined parametric statistical tests are applied in order to evaluate the trend-stationary assumption in global sea surface temperature (SST). 2. Methods Parametric statistical tests have been developed in econometrics for discriminating between difference-stationary and trend-stationary time series. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test ( Kwiatkowski et al. 1992 ) tests the null hypothesis of a trend-stationary process against a difference-stationary alternative. Rejection of the null hypothesis
influence future glacier contributions to global eustatic sea level ( Gregory and Oerlemans 1998 ; Braithwaite and Raper 2002 ; Marshall et al. 2005 ; Bougamont et al. 2005 ; Hanna et al. 2005 ). Mass balance models calculate snow and ice melt using two main approaches: the energy balance approach and the temperature-index or “degree day” approach. The latter approach assumes an empirical relationship between melting and near-surface air temperature, while the former involves the assessment of all
influence future glacier contributions to global eustatic sea level ( Gregory and Oerlemans 1998 ; Braithwaite and Raper 2002 ; Marshall et al. 2005 ; Bougamont et al. 2005 ; Hanna et al. 2005 ). Mass balance models calculate snow and ice melt using two main approaches: the energy balance approach and the temperature-index or “degree day” approach. The latter approach assumes an empirical relationship between melting and near-surface air temperature, while the former involves the assessment of all
1. Introduction Because humanity lives on and obtains its sustenance from the surface of the earth, the near-surface air temperature is often viewed as a critical response variable associated with changes in forcing of the climate system. Several major efforts to create precise, long-term time series of near-surface air temperatures (or simply surface temperatures) have thus been carried out ( Peterson and Vose 1997 ; Hansen et al. 1999 ; Brohan et al. 2006 ). However, problems are apparent
1. Introduction Because humanity lives on and obtains its sustenance from the surface of the earth, the near-surface air temperature is often viewed as a critical response variable associated with changes in forcing of the climate system. Several major efforts to create precise, long-term time series of near-surface air temperatures (or simply surface temperatures) have thus been carried out ( Peterson and Vose 1997 ; Hansen et al. 1999 ; Brohan et al. 2006 ). However, problems are apparent
1. Introduction El Niño–Southern Oscillation (ENSO) induces a strong natural interannual climate signal that affects the surface climate in numerous regions of the globe including the continental United States. The effect of ENSO on the U.S. surface temperature has been documented in many previous studies ( Ropelewski and Halpert 1986 , 1987 ; Kiladis and Diaz 1989 ; Hoerling et al. 1997 ; Larkin and Harrison 2005 ; Wang et al. 2007 ; Lau et al. 2008 ). During El Niño winter, the northern
1. Introduction El Niño–Southern Oscillation (ENSO) induces a strong natural interannual climate signal that affects the surface climate in numerous regions of the globe including the continental United States. The effect of ENSO on the U.S. surface temperature has been documented in many previous studies ( Ropelewski and Halpert 1986 , 1987 ; Kiladis and Diaz 1989 ; Hoerling et al. 1997 ; Larkin and Harrison 2005 ; Wang et al. 2007 ; Lau et al. 2008 ). During El Niño winter, the northern
1. Introduction Earth’s climate change is commonly quantified by variations of global surface temperature (GST). Major GST products include NOAA global surface temperature (NOAAGlobalTemp; Vose et al. 2021 ; Zhang et al. 2019 ; Smith et al. 2008 ), Met Office HadCRUT ( Morice et al. 2021 , 2012 ; Brohan et al. 2006 ), NASA GISTEMP ( Lenssen et al. 2019 ; Hansen et al. 2010 , 1999 ; Hansen and Lebedeff 1987 ), Berkeley Earth Surface temperature (BEST; Rohde and Hausfather 2020
1. Introduction Earth’s climate change is commonly quantified by variations of global surface temperature (GST). Major GST products include NOAA global surface temperature (NOAAGlobalTemp; Vose et al. 2021 ; Zhang et al. 2019 ; Smith et al. 2008 ), Met Office HadCRUT ( Morice et al. 2021 , 2012 ; Brohan et al. 2006 ), NASA GISTEMP ( Lenssen et al. 2019 ; Hansen et al. 2010 , 1999 ; Hansen and Lebedeff 1987 ), Berkeley Earth Surface temperature (BEST; Rohde and Hausfather 2020
various reasons (cost, management complexity, etc.). On the contrary, a good deal of continuously operating surface weather stations cover several areas of the globe, routinely collecting averaged single-level data of wind speed, temperature, humidity, solar radiation, and precipitation. However, modeling fluxes from non-fast-response single-level data implies the introduction of site-dependent parameters (surface roughness, soil–canopy resistances, etc.) that must be otherwise estimated (e.g., Van
various reasons (cost, management complexity, etc.). On the contrary, a good deal of continuously operating surface weather stations cover several areas of the globe, routinely collecting averaged single-level data of wind speed, temperature, humidity, solar radiation, and precipitation. However, modeling fluxes from non-fast-response single-level data implies the introduction of site-dependent parameters (surface roughness, soil–canopy resistances, etc.) that must be otherwise estimated (e.g., Van
