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- Author or Editor: Virginie Guemas x
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Abstract
This article examines the sensitivity of the Laboratoire de Météorologie Dynamique Model with Zoom Capability (LMDZ), a gridpoint atmospheric GCM, to changes in the resolution in latitude and longitude, focusing on the midlatitudes. In a series of dynamical core experiments, increasing the resolution in latitude leads to a poleward shift of the jet, which also becomes less baroclinic, while the maximum eddy variance decreases. The distribution of the jet positions in time also becomes wider. On the contrary, when the resolution increases in longitude, the position and structure of the jet remain almost identical, except for a small equatorward shift tendency. An increase in eddy heat flux is compensated by a strengthening of the Ferrel cell. The source of these distinct behaviors is then explored in constrained experiments in which the zonal-mean zonal wind is constrained toward the same reference state while the resolution varies. While the low-level wave sources always increase with resolution in that case, there is also enhanced poleward propagation when increasing the resolution in longitude, preventing the jet shift. The diverse impacts on the midlatitude dynamics hold when using the full GCM in a realistic setting, either forced by observed SSTs or coupled to an ocean model.
Abstract
This article examines the sensitivity of the Laboratoire de Météorologie Dynamique Model with Zoom Capability (LMDZ), a gridpoint atmospheric GCM, to changes in the resolution in latitude and longitude, focusing on the midlatitudes. In a series of dynamical core experiments, increasing the resolution in latitude leads to a poleward shift of the jet, which also becomes less baroclinic, while the maximum eddy variance decreases. The distribution of the jet positions in time also becomes wider. On the contrary, when the resolution increases in longitude, the position and structure of the jet remain almost identical, except for a small equatorward shift tendency. An increase in eddy heat flux is compensated by a strengthening of the Ferrel cell. The source of these distinct behaviors is then explored in constrained experiments in which the zonal-mean zonal wind is constrained toward the same reference state while the resolution varies. While the low-level wave sources always increase with resolution in that case, there is also enhanced poleward propagation when increasing the resolution in longitude, preventing the jet shift. The diverse impacts on the midlatitude dynamics hold when using the full GCM in a realistic setting, either forced by observed SSTs or coupled to an ocean model.
Abstract
Commonly used statistical tests of hypothesis, also termed inferential tests, that are available to meteorologists and climatologists all require independent data in the time series to which they are applied. However, most of the time series that are usually handled are actually serially dependent. A common approach to handle such a serial dependence is to replace in those statistical tests the actual number of data by an estimated effective number of independent data that is computed from a classical and widely used formula that relies on the autocorrelation function. Despite being perfectly demonstrable under some hypotheses, this formula provides unreliable results on practical cases, for two different reasons. First, the formula has to be applied using the estimated autocorrelation function, which bears a large uncertainty because of the usual shortness of the available time series. After the impact of this uncertainty is illustrated, some recommendations of preliminary treatment of the time series prior to any application of this formula are made. Second, the derivation of this formula is done under the hypothesis of identically distributed data, which is often not valid in real climate or meteorological problems. It is shown how this issue is due to real physical processes that induce temporal coherence, and an illustration is given of how not respecting the hypotheses affects the results provided by the formula.
Abstract
Commonly used statistical tests of hypothesis, also termed inferential tests, that are available to meteorologists and climatologists all require independent data in the time series to which they are applied. However, most of the time series that are usually handled are actually serially dependent. A common approach to handle such a serial dependence is to replace in those statistical tests the actual number of data by an estimated effective number of independent data that is computed from a classical and widely used formula that relies on the autocorrelation function. Despite being perfectly demonstrable under some hypotheses, this formula provides unreliable results on practical cases, for two different reasons. First, the formula has to be applied using the estimated autocorrelation function, which bears a large uncertainty because of the usual shortness of the available time series. After the impact of this uncertainty is illustrated, some recommendations of preliminary treatment of the time series prior to any application of this formula are made. Second, the derivation of this formula is done under the hypothesis of identically distributed data, which is often not valid in real climate or meteorological problems. It is shown how this issue is due to real physical processes that induce temporal coherence, and an illustration is given of how not respecting the hypotheses affects the results provided by the formula.
Abstract
This study investigates the nonlinear processes by which the ocean diurnal variations can affect the intraseasonal sea surface temperature (SST) variability in the Atlantic Ocean. The Centre National de Recherches Météorologiques one-dimensional ocean model (CNRMOM1D) is forced with the 40-yr ECMWF Re-Analysis (ERA-40) surface fluxes with a 1-h frequency in solar heat flux in a first simulation and with a daily forcing frequency in a second simulation. This model has a vertical resolution of 1 m near the surface. The comparison between both experiments shows that the daily mean surface temperature is modified by about 0.3°–0.5°C if the ocean diurnal variations are represented, and this correction can persist for 15–40 days in the midlatitudes and more than 60 days in the tropics. The so-called rectification mechanism, by which the ocean diurnal warming enhances the intraseasonal SST variability by 20%–40%, is found to be robust in the tropics. In contrast, in the midlatitudes, diurnal variations in wind stress and nonsolar heat flux are shown to affect the daily mean SST. For example, an intense wind stress or nonsolar heat flux toward the atmosphere during the first half of the day followed by weak fluxes during the second half result in a shallow mixed layer. The following day, the preconditioning results in heat being trapped near the surface and the daily mean surface temperature being higher than if these diurnal variations in surface forcings were not resolved.
Abstract
This study investigates the nonlinear processes by which the ocean diurnal variations can affect the intraseasonal sea surface temperature (SST) variability in the Atlantic Ocean. The Centre National de Recherches Météorologiques one-dimensional ocean model (CNRMOM1D) is forced with the 40-yr ECMWF Re-Analysis (ERA-40) surface fluxes with a 1-h frequency in solar heat flux in a first simulation and with a daily forcing frequency in a second simulation. This model has a vertical resolution of 1 m near the surface. The comparison between both experiments shows that the daily mean surface temperature is modified by about 0.3°–0.5°C if the ocean diurnal variations are represented, and this correction can persist for 15–40 days in the midlatitudes and more than 60 days in the tropics. The so-called rectification mechanism, by which the ocean diurnal warming enhances the intraseasonal SST variability by 20%–40%, is found to be robust in the tropics. In contrast, in the midlatitudes, diurnal variations in wind stress and nonsolar heat flux are shown to affect the daily mean SST. For example, an intense wind stress or nonsolar heat flux toward the atmosphere during the first half of the day followed by weak fluxes during the second half result in a shallow mixed layer. The following day, the preconditioning results in heat being trapped near the surface and the daily mean surface temperature being higher than if these diurnal variations in surface forcings were not resolved.
Abstract
The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.
Abstract
The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.
