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- Author or Editor: Dietmar Dommenget x
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Abstract
Uncertainties in the numerical realization of the physical climate system in coarse-resolution climate models in the Coupled Model Intercomparison Project phase 3 (CMIP3) cause large spread in the global mean and regional response amplitude to a given anthropogenic forcing scenario, and they cause the climate models to have mean state climates different from the observed and different from each other. In a series of sensitivity simulations with an atmospheric general circulation model coupled to a Slab Ocean Model, the role of differences in the control mean sea surface temperature (SST) in simulating the global mean and regional response amplitude is explored. The model simulations are forced into the control mean state SST of 24 CMIP3 climate models, and 2xCO2 forcing experiments are started from the different control states. The differences in the SST mean state cause large differences in other climate variables, but they do not reproduce most of the large spread in the mean state climate over land and ice-covered regions found in the CMIP3 model simulations. The spread in the mean SST climatology leads to a spread in the global mean and regional response amplitude of about 10%, which is about half as much as the spread in the response of the CMIP3 climate models and is therefore of considerable size. Since the SST climatology biases are only a small part of the models’ mean state climate biases, it is likely that the climate model’s mean state climate biases are accounting for a large part of the model’s climate sensitivity spread.
Abstract
Uncertainties in the numerical realization of the physical climate system in coarse-resolution climate models in the Coupled Model Intercomparison Project phase 3 (CMIP3) cause large spread in the global mean and regional response amplitude to a given anthropogenic forcing scenario, and they cause the climate models to have mean state climates different from the observed and different from each other. In a series of sensitivity simulations with an atmospheric general circulation model coupled to a Slab Ocean Model, the role of differences in the control mean sea surface temperature (SST) in simulating the global mean and regional response amplitude is explored. The model simulations are forced into the control mean state SST of 24 CMIP3 climate models, and 2xCO2 forcing experiments are started from the different control states. The differences in the SST mean state cause large differences in other climate variables, but they do not reproduce most of the large spread in the mean state climate over land and ice-covered regions found in the CMIP3 model simulations. The spread in the mean SST climatology leads to a spread in the global mean and regional response amplitude of about 10%, which is about half as much as the spread in the response of the CMIP3 climate models and is therefore of considerable size. Since the SST climatology biases are only a small part of the models’ mean state climate biases, it is likely that the climate model’s mean state climate biases are accounting for a large part of the model’s climate sensitivity spread.
Abstract
In a recent article, Dommenget discussed the role of sea surface temperature variability for continental climate variability and change. Lambert et al. comment on Dommenget’s article in their article several times, arguing that the sensitivity experiment in Dommenget, in which the SST response to surface land temperature changes are discussed, is inconsistent with their and other previously published studies. In this comment, the results of Dommenget’s sensitivity experiments are discussed in more detail and the experiments are extended for longer response times. It is shown that the discussion of how the oceans’ response to land forcing is time-scale dependent, with a very weak response to land forcing on interannual time scales, as discussed in Dommenget, and that it has about a twice as strong of a near-equilibrium response to land forcing on time scales longer than 100 yr. The asymmetric land–sea interaction, with the ocean forcing the land much more strongly than the land forces the oceans, as discussed in Dommenget, is confirmed by this study.
Abstract
In a recent article, Dommenget discussed the role of sea surface temperature variability for continental climate variability and change. Lambert et al. comment on Dommenget’s article in their article several times, arguing that the sensitivity experiment in Dommenget, in which the SST response to surface land temperature changes are discussed, is inconsistent with their and other previously published studies. In this comment, the results of Dommenget’s sensitivity experiments are discussed in more detail and the experiments are extended for longer response times. It is shown that the discussion of how the oceans’ response to land forcing is time-scale dependent, with a very weak response to land forcing on interannual time scales, as discussed in Dommenget, and that it has about a twice as strong of a near-equilibrium response to land forcing on time scales longer than 100 yr. The asymmetric land–sea interaction, with the ocean forcing the land much more strongly than the land forces the oceans, as discussed in Dommenget, is confirmed by this study.
Abstract
A characteristic feature of global warming is the land–sea contrast, with stronger warming over land than over oceans. Recent studies find that this land–sea contrast also exists in equilibrium global change scenarios, and it is caused by differences in the availability of surface moisture over land and oceans. In this study it is illustrated that this land–sea contrast exists also on interannual time scales and that the ocean–land interaction is strongly asymmetric. The land surface temperature is more sensitive to the oceans than the oceans are to the land surface temperature, which is related to the processes causing the land–sea contrast in global warming scenarios. It suggests that the ocean’s natural variability and change is leading to variability and change with enhanced magnitudes over the continents, causing much of the longer-time-scale (decadal) global-scale continental climate variability. Model simulations illustrate that continental warming due to anthropogenic forcing (e.g., the warming at the end of the last century or future climate change scenarios) is mostly (80%–90%) indirectly forced by the contemporaneous ocean warming, not directly by local radiative forcing.
Abstract
A characteristic feature of global warming is the land–sea contrast, with stronger warming over land than over oceans. Recent studies find that this land–sea contrast also exists in equilibrium global change scenarios, and it is caused by differences in the availability of surface moisture over land and oceans. In this study it is illustrated that this land–sea contrast exists also on interannual time scales and that the ocean–land interaction is strongly asymmetric. The land surface temperature is more sensitive to the oceans than the oceans are to the land surface temperature, which is related to the processes causing the land–sea contrast in global warming scenarios. It suggests that the ocean’s natural variability and change is leading to variability and change with enhanced magnitudes over the continents, causing much of the longer-time-scale (decadal) global-scale continental climate variability. Model simulations illustrate that continental warming due to anthropogenic forcing (e.g., the warming at the end of the last century or future climate change scenarios) is mostly (80%–90%) indirectly forced by the contemporaneous ocean warming, not directly by local radiative forcing.
Abstract
Analyses of annual mean sea surface temperatures (SST) from observations for the period 1903–94 and four different general circulation models (GCMs) were conducted. The two dominant EOFs of all datasets are characterized by two patterns, which are centered in the trade wind zones, at roughly 15°N and 15°S, respectively. The two patterns are uncorrelated at any lag and the time spectra of the corresponding principle components are consistent with red noise. The SST variability is strongly correlated with wind stress anomalies in the trade wind zones. The correlations between the wind stress and the SST, as well as the correlation between the net heat flux and the SST anomalies are consistent with the assumption that the variability of the upper tropical Atlantic Ocean is forced by the atmosphere. Dynamic feedbacks of the tropical Atlantic Ocean are less important. The variability in the trade wind zones shows a weak correlation with the ENSO mode in the tropical Pacific.
Abstract
Analyses of annual mean sea surface temperatures (SST) from observations for the period 1903–94 and four different general circulation models (GCMs) were conducted. The two dominant EOFs of all datasets are characterized by two patterns, which are centered in the trade wind zones, at roughly 15°N and 15°S, respectively. The two patterns are uncorrelated at any lag and the time spectra of the corresponding principle components are consistent with red noise. The SST variability is strongly correlated with wind stress anomalies in the trade wind zones. The correlations between the wind stress and the SST, as well as the correlation between the net heat flux and the SST anomalies are consistent with the assumption that the variability of the upper tropical Atlantic Ocean is forced by the atmosphere. Dynamic feedbacks of the tropical Atlantic Ocean are less important. The variability in the trade wind zones shows a weak correlation with the ENSO mode in the tropical Pacific.
Abstract
This article addresses the causes of the large-scale tropical sea level pressure (SLP) changes during climate change. The analysis presented here is based on model simulations, observed trends, and the seasonal cycle. In all three cases the regional changes of tropospheric temperature (T tropos) and SLP are strongly related to each other [considerably more strongly than (sea) surface temperature and SLP]. This relationship basically follows the Bjerknes circulation theorem, with relatively low regional SLP where there is relatively high T tropos and vice versa. A simple physical model suggests a tropical SLP response to horizontally inhomogeneous warming in the tropical T tropos, with a sensitivity coefficient of about −1.7 hPa K−1. This relationship explains a large fraction of observed and predicted changes in the tropical SLP.
It is shown that in climate change model simulations the tropospheric land–sea warming contrast is the most significant structure in the regional T tropos changes relative to the tropical mean changes. Since the land–sea warming contrast exists in the absence of any atmospheric circulation changes, it can be argued that the large-scale response of tropical SLP changes is to first order a response to the tropical land–sea warming contrast. Furthermore, as the land–sea warming contrast is mostly moisture dependent, the models predict a stronger warming and decreasing SLP in the drier regions from South America to Africa and a weaker warming and increasing SLP over the wetter Indo-Pacific warm pool region. This suggests an increase in the potential for deep convection conditions over the Atlantic sector and a decrease over the Indo-Pacific warm pool region in the future.
Abstract
This article addresses the causes of the large-scale tropical sea level pressure (SLP) changes during climate change. The analysis presented here is based on model simulations, observed trends, and the seasonal cycle. In all three cases the regional changes of tropospheric temperature (T tropos) and SLP are strongly related to each other [considerably more strongly than (sea) surface temperature and SLP]. This relationship basically follows the Bjerknes circulation theorem, with relatively low regional SLP where there is relatively high T tropos and vice versa. A simple physical model suggests a tropical SLP response to horizontally inhomogeneous warming in the tropical T tropos, with a sensitivity coefficient of about −1.7 hPa K−1. This relationship explains a large fraction of observed and predicted changes in the tropical SLP.
It is shown that in climate change model simulations the tropospheric land–sea warming contrast is the most significant structure in the regional T tropos changes relative to the tropical mean changes. Since the land–sea warming contrast exists in the absence of any atmospheric circulation changes, it can be argued that the large-scale response of tropical SLP changes is to first order a response to the tropical land–sea warming contrast. Furthermore, as the land–sea warming contrast is mostly moisture dependent, the models predict a stronger warming and decreasing SLP in the drier regions from South America to Africa and a weaker warming and increasing SLP over the wetter Indo-Pacific warm pool region. This suggests an increase in the potential for deep convection conditions over the Atlantic sector and a decrease over the Indo-Pacific warm pool region in the future.
Abstract
Several recent general circulation model studies discuss the predictability of the Indian Ocean dipole (IOD) mode, suggesting that it is predictable because of coupled ocean–atmosphere interactions in the Indian Ocean. However, it is not clear from these studies how much of the predictability is due to the response to El Niño. It is shown in this note that a simple statistical model that treats the Indian Ocean as a red noise process forced by tropical Pacific SST shows forecast skills comparable to those of recent general circulation model studies. The results also indicate that some of the eastern tropical Indian Ocean SST predictability in recent studies may indeed be beyond the skill of the simple model proposed in this note, indicating that dynamics in the Indian Ocean may have caused this improved predictability in this region. The model further indicates that the IOD index may be the least predictable index of Indian Ocean SST variability. The model is proposed as a null hypothesis for Indian Ocean SST predictions.
Abstract
Several recent general circulation model studies discuss the predictability of the Indian Ocean dipole (IOD) mode, suggesting that it is predictable because of coupled ocean–atmosphere interactions in the Indian Ocean. However, it is not clear from these studies how much of the predictability is due to the response to El Niño. It is shown in this note that a simple statistical model that treats the Indian Ocean as a red noise process forced by tropical Pacific SST shows forecast skills comparable to those of recent general circulation model studies. The results also indicate that some of the eastern tropical Indian Ocean SST predictability in recent studies may indeed be beyond the skill of the simple model proposed in this note, indicating that dynamics in the Indian Ocean may have caused this improved predictability in this region. The model further indicates that the IOD index may be the least predictable index of Indian Ocean SST variability. The model is proposed as a null hypothesis for Indian Ocean SST predictions.
Abstract
Simulations and seasonal forecasts of tropical Pacific SST and subsurface fields that are based on the global Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) ocean-state estimation procedure are investigated. As compared to similar results from a traditional ENSO simulation and forecast procedure, the hindcast of the constrained ocean state is significantly closer to observed surface and subsurface conditions. The skill of the 12-month lead SST forecast in the equatorial Pacific is comparable in both approaches. The optimization appears to have better skill in the SST anomaly correlations, suggesting that the initial ocean conditions and forcing corrections calculated by the ocean-state estimation do have a positive impact on the predictive skill. However, the optimized forecast skill is currently limited by the low quality of the statistical atmosphere. Progress is expected from optimizing a coupled model over a longer time interval with the coupling statistics being part of the control vector.
Abstract
Simulations and seasonal forecasts of tropical Pacific SST and subsurface fields that are based on the global Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) ocean-state estimation procedure are investigated. As compared to similar results from a traditional ENSO simulation and forecast procedure, the hindcast of the constrained ocean state is significantly closer to observed surface and subsurface conditions. The skill of the 12-month lead SST forecast in the equatorial Pacific is comparable in both approaches. The optimization appears to have better skill in the SST anomaly correlations, suggesting that the initial ocean conditions and forcing corrections calculated by the ocean-state estimation do have a positive impact on the predictive skill. However, the optimized forecast skill is currently limited by the low quality of the statistical atmosphere. Progress is expected from optimizing a coupled model over a longer time interval with the coupling statistics being part of the control vector.
Abstract
Empirical orthogonal function (EOF) analyses (rotated or not) are widely used in climate research. In recent years there have been several studies in which EOF analyses were used to highlight potential physical mechanisms associated with climate variability. For example, several SST modes were identified such as the “Tropical Atlantic Dipole,” the “Tropical Indian Ocean Dipole,” and different SLP modes in the Northern Hemisphere winter. In this note it is emphasized that caution should be used when trying to interpret these statistically derived modes and their significance. Indeed, from a synthetic example it is shown that patterns derived from EOF analyses can be misleading at times and associated with very little climate physics.
Abstract
Empirical orthogonal function (EOF) analyses (rotated or not) are widely used in climate research. In recent years there have been several studies in which EOF analyses were used to highlight potential physical mechanisms associated with climate variability. For example, several SST modes were identified such as the “Tropical Atlantic Dipole,” the “Tropical Indian Ocean Dipole,” and different SLP modes in the Northern Hemisphere winter. In this note it is emphasized that caution should be used when trying to interpret these statistically derived modes and their significance. Indeed, from a synthetic example it is shown that patterns derived from EOF analyses can be misleading at times and associated with very little climate physics.
Abstract
This study utilizes observations and a series of idealized experiments to explore whether eastern Pacific (EP)- and central Pacific (CP)-type El Niño–Southern Oscillation (ENSO) events produce surface wind stress responses with distinct spatial structures. We find that the meridionally broader sea surface temperatures (SSTs) during CP events lead to zonal wind stresses that are also meridionally broader than those found during EP-type events, leading to differences in the near-equatorial wind stress curl. These wind spatial structure differences create differences in the associated pre- and post-ENSO event WWV response. For instance, the meridionally narrow winds found during EP events have (i) weaker wind stresses along 5°N and 5°S, leading to weaker Ekman-induced pre-event WWV changes; and (ii) stronger near-equatorial wind stress curls that lead to a much larger post-ENSO event WWV changes than during CP events. The latter suggests that, in the framework of the recharge oscillator model, the EP events have stronger coupling between sea surface temperatures (SST) and thermocline (WWV), supporting more clearly the phase transition of ENSO events, and therefore, the oscillating nature of ENSO than CP events. The results suggest that the spatial structure of the SST pattern and the related differences in the wind stress curl, are required along with equatorial wind stress to accurately model the WWV changes during EP- and CP-type ENSO events.
Abstract
This study utilizes observations and a series of idealized experiments to explore whether eastern Pacific (EP)- and central Pacific (CP)-type El Niño–Southern Oscillation (ENSO) events produce surface wind stress responses with distinct spatial structures. We find that the meridionally broader sea surface temperatures (SSTs) during CP events lead to zonal wind stresses that are also meridionally broader than those found during EP-type events, leading to differences in the near-equatorial wind stress curl. These wind spatial structure differences create differences in the associated pre- and post-ENSO event WWV response. For instance, the meridionally narrow winds found during EP events have (i) weaker wind stresses along 5°N and 5°S, leading to weaker Ekman-induced pre-event WWV changes; and (ii) stronger near-equatorial wind stress curls that lead to a much larger post-ENSO event WWV changes than during CP events. The latter suggests that, in the framework of the recharge oscillator model, the EP events have stronger coupling between sea surface temperatures (SST) and thermocline (WWV), supporting more clearly the phase transition of ENSO events, and therefore, the oscillating nature of ENSO than CP events. The results suggest that the spatial structure of the SST pattern and the related differences in the wind stress curl, are required along with equatorial wind stress to accurately model the WWV changes during EP- and CP-type ENSO events.
