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indicate systematically earlier melt onset after 2000. This is consistent with average June 850-hPa temperatures over the three regions from the NCEP–NCAR reanalysis, which increased by 0.24°C (Svalbard), 0.33°C (Novaya Zemlya), and 0.43°C (Severnaya Zemlya) between 1992–2000 and 2000–04. However, regression analysis of the relationships between melt season duration and June 850-hPa temperatures over the three regions suggests sensitivities of between 3.24 days °C −1 (Severnaya Zemlya) and 7.4 days °C
indicate systematically earlier melt onset after 2000. This is consistent with average June 850-hPa temperatures over the three regions from the NCEP–NCAR reanalysis, which increased by 0.24°C (Svalbard), 0.33°C (Novaya Zemlya), and 0.43°C (Severnaya Zemlya) between 1992–2000 and 2000–04. However, regression analysis of the relationships between melt season duration and June 850-hPa temperatures over the three regions suggests sensitivities of between 3.24 days °C −1 (Severnaya Zemlya) and 7.4 days °C
750T ( Fig. 8 ). This relationship was used to develop simple empirical models to predict daily near-surface lapse rates from standardized daily anomalies in 750T (summer means removed and divided through by respective standard deviation). Using least squares regression analysis, model coefficients were calculated for each of the four glaciers ( Table 3 ). Because the regression models are based on standardized anomalies (mean = 0), all model y intercepts ( β ) are equal to respective glacier
750T ( Fig. 8 ). This relationship was used to develop simple empirical models to predict daily near-surface lapse rates from standardized daily anomalies in 750T (summer means removed and divided through by respective standard deviation). Using least squares regression analysis, model coefficients were calculated for each of the four glaciers ( Table 3 ). Because the regression models are based on standardized anomalies (mean = 0), all model y intercepts ( β ) are equal to respective glacier
Pacific is mainly salt driven (rather than temperature driven, as it is on seasonal time scales). The bottom line of this EOF analysis of the North Pacific from CCSM3 is that salinity is tightly coupled to the temperature in the PDO and can apparently play an important role in the decadal variability characteristic of the North Pacific Ocean. A more complete description of the mechanism involved is provided in the following sections. 5. Timing of the PDO We describe the time-lag regressions on the PDO
Pacific is mainly salt driven (rather than temperature driven, as it is on seasonal time scales). The bottom line of this EOF analysis of the North Pacific from CCSM3 is that salinity is tightly coupled to the temperature in the PDO and can apparently play an important role in the decadal variability characteristic of the North Pacific Ocean. A more complete description of the mechanism involved is provided in the following sections. 5. Timing of the PDO We describe the time-lag regressions on the PDO
anthropogenically forced global warming. The available observations, both instrumental records as well as climate proxies, are too sparse to serve as a means to identify the mechanisms that lead to the occurrence of such low-frequency climate variability in the North Atlantic basin. We are therefore obliged to resort to the analysis of model simulations. From this perspective, it has been clear even on the basis of the earliest applications of global coupled ocean–atmosphere general circulation models based on
anthropogenically forced global warming. The available observations, both instrumental records as well as climate proxies, are too sparse to serve as a means to identify the mechanisms that lead to the occurrence of such low-frequency climate variability in the North Atlantic basin. We are therefore obliged to resort to the analysis of model simulations. From this perspective, it has been clear even on the basis of the earliest applications of global coupled ocean–atmosphere general circulation models based on
regression of data from 63 mountain glaciers and yielded γ = 1.357 as the best-fit value for the exponent in (1) . By elegant use of dimensional analysis, Bahr (1997) and Bahr et al. (1997) derived a theoretical value of γ = 11/8 = 1.375 for the exponent, in remarkable agreement with Chen and Ohmura (1990) . However, the coefficient c in (1) remains beyond the reach of dimensional analysis and must be treated as a fitting parameter that can differ from region to region. Several physically
regression of data from 63 mountain glaciers and yielded γ = 1.357 as the best-fit value for the exponent in (1) . By elegant use of dimensional analysis, Bahr (1997) and Bahr et al. (1997) derived a theoretical value of γ = 11/8 = 1.375 for the exponent, in remarkable agreement with Chen and Ohmura (1990) . However, the coefficient c in (1) remains beyond the reach of dimensional analysis and must be treated as a fitting parameter that can differ from region to region. Several physically
