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- Author or Editor: William J. Merryfield x
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Abstract
An EOF analysis is used to intercompare the response of ENSO-like variability to CO2 doubling in results from 15 coupled climate models assembled for the Intergovernmental Panel on Climate Change Fourth Assessment Report. Under preindustrial conditions, 12 of the 15 models exhibit ENSO amplitudes comparable to or exceeding that observed in the second half of the twentieth century. Under CO2 doubling, three of the models exhibit statistically significant (p < 0.1) increases in ENSO amplitude, and five exhibit significant decreases. The overall amplitude changes are not strongly related to the magnitude or pattern of surface warming. It is, however, found that ENSO amplitude decreases (increases) in models having a narrow (wide) ENSO zonal wind stress response and ENSO amplitude comparable to or greater than observed. The models exhibit a mean fractional decrease in ENSO period of about 5%. Although many factors can influence the ENSO period, it is suggested that this may be related to a comparable increase in equatorial wave speed through an associated speedup of delayed-oscillator feedback. Changes in leading EOF, characterized in many of the models by a relative increase in the amplitude of SST variations in the central Pacific, are in most cases consistent with effects of anomalous zonal and vertical advection resulting from warming-induced changes in SST.
Abstract
An EOF analysis is used to intercompare the response of ENSO-like variability to CO2 doubling in results from 15 coupled climate models assembled for the Intergovernmental Panel on Climate Change Fourth Assessment Report. Under preindustrial conditions, 12 of the 15 models exhibit ENSO amplitudes comparable to or exceeding that observed in the second half of the twentieth century. Under CO2 doubling, three of the models exhibit statistically significant (p < 0.1) increases in ENSO amplitude, and five exhibit significant decreases. The overall amplitude changes are not strongly related to the magnitude or pattern of surface warming. It is, however, found that ENSO amplitude decreases (increases) in models having a narrow (wide) ENSO zonal wind stress response and ENSO amplitude comparable to or greater than observed. The models exhibit a mean fractional decrease in ENSO period of about 5%. Although many factors can influence the ENSO period, it is suggested that this may be related to a comparable increase in equatorial wave speed through an associated speedup of delayed-oscillator feedback. Changes in leading EOF, characterized in many of the models by a relative increase in the amplitude of SST variations in the central Pacific, are in most cases consistent with effects of anomalous zonal and vertical advection resulting from warming-induced changes in SST.
Abstract
Intrusions like those observed in double-diffusively stable regions of the Arctic Ocean can grow from uniform ambient temperature and salinity gradients if diapycnal mixing of these two components differs. Assuming this to be the driving mechanism, the observed 40–60 m intrusion heights constrain the turbulent diffusivity for heat to be less than about 0.01 cm2 s−1 and the salt-to-heat turbulent diffusivity ratio to be greater than about 0.6 if the diffusivities are constant. Observations indicate that the intrusions slope across isopycnals in a sense that is consistent with such a scenario, although the along-intrusion density ratio is greater than that predicted by linear theory for the fastest-growing intrusions. Numerical solutions for growing intrusions resemble observed temperature and salinity profiles.
Abstract
Intrusions like those observed in double-diffusively stable regions of the Arctic Ocean can grow from uniform ambient temperature and salinity gradients if diapycnal mixing of these two components differs. Assuming this to be the driving mechanism, the observed 40–60 m intrusion heights constrain the turbulent diffusivity for heat to be less than about 0.01 cm2 s−1 and the salt-to-heat turbulent diffusivity ratio to be greater than about 0.6 if the diffusivities are constant. Observations indicate that the intrusions slope across isopycnals in a sense that is consistent with such a scenario, although the along-intrusion density ratio is greater than that predicted by linear theory for the fastest-growing intrusions. Numerical solutions for growing intrusions resemble observed temperature and salinity profiles.
Abstract
Mechanisms and parameter dependence of differential mixing of heat and salt by ocean turbulence are investigated numerically by extending a previous study to examine dependence upon buoyancy frequency N and density gradient ratio Rρ . In these experiments a burst of turbulence mixes temperature T and pseudosalinity S having molecular diffusivity 0.1 times that of T across background vertical gradients of both quantities. In contrast to previous results, which found turbulent diffusivity ratios d = KS /KT < 1 at a fixed N, the present study finds that d > 1 when N = 0 and that d tends to approach this value as N → 0. In all cases considered, d is larger at high Rρ (buoyancy dominated by T) than at low Rρ (buoyancy dominated by S). It is shown that this tendency is consistent with differential mixing being largely due to preferential restratification of the slower-diffusing component S. This conclusion is reinforced by the finding that d scales linearly with a fractional restratification measure over a wide range of conditions.
Abstract
Mechanisms and parameter dependence of differential mixing of heat and salt by ocean turbulence are investigated numerically by extending a previous study to examine dependence upon buoyancy frequency N and density gradient ratio Rρ . In these experiments a burst of turbulence mixes temperature T and pseudosalinity S having molecular diffusivity 0.1 times that of T across background vertical gradients of both quantities. In contrast to previous results, which found turbulent diffusivity ratios d = KS /KT < 1 at a fixed N, the present study finds that d > 1 when N = 0 and that d tends to approach this value as N → 0. In all cases considered, d is larger at high Rρ (buoyancy dominated by T) than at low Rρ (buoyancy dominated by S). It is shown that this tendency is consistent with differential mixing being largely due to preferential restratification of the slower-diffusing component S. This conclusion is reinforced by the finding that d scales linearly with a fractional restratification measure over a wide range of conditions.
Abstract
Hypotheses concerning the origin of thermohaline staircases in salt fingering regions are reviewed and assessed. One such hypothesis, that staircases arise from thermohaline intrusions, is developed into a quantitative theory. It is shown that growing intrusions evolve toward staircases when the background density ratio lies below a threshold value, and nonlinear computations confirm that staircases are viable intrusion equilibria. Staircase properties such as step heights, lateral density ratios, and layer slopes lie closest to observed values when salt fingers are assumed not to contribute to shear stress and when turbulent mixing rates are smaller than usual thermocline values.
Abstract
Hypotheses concerning the origin of thermohaline staircases in salt fingering regions are reviewed and assessed. One such hypothesis, that staircases arise from thermohaline intrusions, is developed into a quantitative theory. It is shown that growing intrusions evolve toward staircases when the background density ratio lies below a threshold value, and nonlinear computations confirm that staircases are viable intrusion equilibria. Staircase properties such as step heights, lateral density ratios, and layer slopes lie closest to observed values when salt fingers are assumed not to contribute to shear stress and when turbulent mixing rates are smaller than usual thermocline values.
Abstract
Variability of subtropical cell (STC) overturning in the upper Pacific Ocean is examined in a coupled climate model in light of large observed changes in STC transport. In a 1000-yr control run, modeled STC variations are smaller than observed, but correlate in a similar way with low-frequency ENSO-like variability. In model runs that include anthropogenically forced climate change, STC pycnocline transports decrease progressively under the influence of global warming, attaining reductions of 8% by 2000 and 46% by 2100. Although the former reduction is insufficient to fully account for the apparent observed decline in STC transport over recent decades, it does suggest that global warming may have contributed to the observed changes. Analysis of coupled model results shows that STC transports play a significant role in modulating tropical Pacific Ocean heat content, and that such changes are dominated by anomalous currents advecting mean temperature, rather than by advection of temperature anomalies by mean currents.
Abstract
Variability of subtropical cell (STC) overturning in the upper Pacific Ocean is examined in a coupled climate model in light of large observed changes in STC transport. In a 1000-yr control run, modeled STC variations are smaller than observed, but correlate in a similar way with low-frequency ENSO-like variability. In model runs that include anthropogenically forced climate change, STC pycnocline transports decrease progressively under the influence of global warming, attaining reductions of 8% by 2000 and 46% by 2100. Although the former reduction is insufficient to fully account for the apparent observed decline in STC transport over recent decades, it does suggest that global warming may have contributed to the observed changes. Analysis of coupled model results shows that STC transports play a significant role in modulating tropical Pacific Ocean heat content, and that such changes are dominated by anomalous currents advecting mean temperature, rather than by advection of temperature anomalies by mean currents.
Abstract
This study examines the changing roles of temperature and precipitation on snowpack variability in the Northern Hemisphere for Second Generation Canadian Earth System Model (CanESM2) historical (1850–2005) and future (2006–2100) climate simulations. The strength of the linear relationship between monthly snow water equivalent (SWE) in January–April and precipitation P or temperature T predictors is found to be a sigmoidal function of the mean temperature over the snow season up to the indicated month. For P predictors, the strength of this relationship increases for colder snow seasons, whereas for T predictors it increases for warmer snow seasons. These behaviors are largely explained by the daily temperature percentiles below freezing during the snow accumulation period. It is found that there is a threshold temperature (−5±1°C, depending on month in the snow season and largely independent of emission scenario), representing a crossover point below which snow seasons are sufficiently cold that P is the primary driver of snowpack amount and above which T is the primary driver. This isotherm allows one to delineate the snow-climate regions and elevation zones in which snow-cover amounts are more vulnerable to a warming climate. As climate projections indicate that seasonal isotherms shift northward and toward higher elevations, regions where snowpack amount is mainly driven by precipitation recede, whereas temperature-sensitive snow-covered areas extend to higher latitudes and/or elevations, with resulting impacts on ecosystems and society.
Abstract
This study examines the changing roles of temperature and precipitation on snowpack variability in the Northern Hemisphere for Second Generation Canadian Earth System Model (CanESM2) historical (1850–2005) and future (2006–2100) climate simulations. The strength of the linear relationship between monthly snow water equivalent (SWE) in January–April and precipitation P or temperature T predictors is found to be a sigmoidal function of the mean temperature over the snow season up to the indicated month. For P predictors, the strength of this relationship increases for colder snow seasons, whereas for T predictors it increases for warmer snow seasons. These behaviors are largely explained by the daily temperature percentiles below freezing during the snow accumulation period. It is found that there is a threshold temperature (−5±1°C, depending on month in the snow season and largely independent of emission scenario), representing a crossover point below which snow seasons are sufficiently cold that P is the primary driver of snowpack amount and above which T is the primary driver. This isotherm allows one to delineate the snow-climate regions and elevation zones in which snow-cover amounts are more vulnerable to a warming climate. As climate projections indicate that seasonal isotherms shift northward and toward higher elevations, regions where snowpack amount is mainly driven by precipitation recede, whereas temperature-sensitive snow-covered areas extend to higher latitudes and/or elevations, with resulting impacts on ecosystems and society.
Abstract
The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.
Abstract
The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.
Abstract
A promising means for increasing skill of seasonal predictions of Arctic sea ice is improving sea ice thickness (SIT) initial conditions; however, sparse SIT observations limit this potential. Using the Canadian Climate Model, version 3 (CanCM3), three statistical models designed to estimate SIT fields for initialization in a real-time forecasting system are applied to initialize sea ice hindcasts over 1981–2012. Hindcast skill is assessed relative to two benchmark SIT initialization methods (SIT-IMs): a climatological initialization currently used operationally and SIT values from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Based on several measures of skill, sea ice predictions are generally improved relative to a climatological initialization. The accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with SIT-IMs that best represent both processes. Similar skill to that achieved by initializing with PIOMAS, including skillful predictions of detrended September SIA from May, is obtained by initializing with two of the statistical models. Regional skill for September SIC is also enhanced using improved SIT-IMs, with an increase in the spatial coverage of statistically significant skill from 10% to 60%–70% of the appreciably varying ice pack. Reduced skill is seen, however, in the Nordic seas using the improved SIT-IMs, resulting from an inherent cold sea surface temperature bias in CanCM3 that is amplified by a thicker initial ice cover.
Abstract
A promising means for increasing skill of seasonal predictions of Arctic sea ice is improving sea ice thickness (SIT) initial conditions; however, sparse SIT observations limit this potential. Using the Canadian Climate Model, version 3 (CanCM3), three statistical models designed to estimate SIT fields for initialization in a real-time forecasting system are applied to initialize sea ice hindcasts over 1981–2012. Hindcast skill is assessed relative to two benchmark SIT initialization methods (SIT-IMs): a climatological initialization currently used operationally and SIT values from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Based on several measures of skill, sea ice predictions are generally improved relative to a climatological initialization. The accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with SIT-IMs that best represent both processes. Similar skill to that achieved by initializing with PIOMAS, including skillful predictions of detrended September SIA from May, is obtained by initializing with two of the statistical models. Regional skill for September SIC is also enhanced using improved SIT-IMs, with an increase in the spatial coverage of statistically significant skill from 10% to 60%–70% of the appreciably varying ice pack. Reduced skill is seen, however, in the Nordic seas using the improved SIT-IMs, resulting from an inherent cold sea surface temperature bias in CanCM3 that is amplified by a thicker initial ice cover.
Abstract
Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.
Abstract
Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.
Abstract
This study evaluates the ability of global climate models to reproduce observed tropical influences on North Pacific Ocean sea surface temperature variability. In an ensemble of climate models, the study finds that the simulated North Pacific response to El Niño–Southern Oscillation (ENSO) forcing is systematically delayed relative to the observed response because of winter and spring mixed layers in the North Pacific that are too deep and air–sea feedbacks that are too weak. Model biases in mixed layer depth and air–sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by about 30%. The study also shows that simulated North Pacific variability has more power at lower frequencies than is observed because of model errors originating in the tropics and extratropics. Implications of these results for predictions on seasonal, decadal, and longer time scales are discussed.
Abstract
This study evaluates the ability of global climate models to reproduce observed tropical influences on North Pacific Ocean sea surface temperature variability. In an ensemble of climate models, the study finds that the simulated North Pacific response to El Niño–Southern Oscillation (ENSO) forcing is systematically delayed relative to the observed response because of winter and spring mixed layers in the North Pacific that are too deep and air–sea feedbacks that are too weak. Model biases in mixed layer depth and air–sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by about 30%. The study also shows that simulated North Pacific variability has more power at lower frequencies than is observed because of model errors originating in the tropics and extratropics. Implications of these results for predictions on seasonal, decadal, and longer time scales are discussed.
