Search Results
Abstract
Numerical experiments are performed to examine the causes of variability of Atlantic Ocean SST during the period covered by the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (1958–98). Three ocean models are used. Two are mixed layer models: one with a 75-m-deep mixed layer and the other with a variable depth mixed layer. For both mixed layer models the ocean heat transports are assumed to remain at their diagnosed climatological values. The third model is a full dynamical ocean general circulation model (GCM). All models are coupled to a model of the subcloud atmospheric mixed layer (AML). The AML model computes the air temperature and humidity by balancing surface fluxes, radiative cooling, entrainment at cloud base, advection and eddy heat, and moisture transports. The models are forced with NCEP–NCAR monthly mean winds from 1958 to 1998.
The ocean mixed layer models adequately reproduce the dominant pattern of Atlantic Ocean climate variability in both its spatial pattern and time dependence. This pattern is the familiar tripole of alternating zonal bands of SST anomalies stretching between the subpolar gyre and the subtropics. This SST pattern goes along with a wind pattern that corresponds to the North Atlantic Oscillation (NAO). Analysis of the results reveals that changes in wind speed create the subtropical SST anomalies while at higher latitudes changes in advection of temperature and humidity and changes in atmospheric eddy fluxes are important.
An observational analysis of the boundary layer energy balance is also performed. Anomalous atmospheric eddy heat fluxes are very closely tied to the SST anomalies. Anomalous horizontal eddy fluxes damp the SST anomalies while anomalous vertical eddy fluxes tend to cool the entire midlatitude North Atlantic during the NAO’s high-index phase with the maximum cooling exactly where the SST gradient is strengthened the most.
The SSTs simulated by the ocean mixed layer model are compared with those simulated by the dynamic ocean GCM. In the far North Atlantic Ocean anomalous ocean heat transports are equally important as surface fluxes in generating SST anomalies and they act constructively. The anomalous heat transports are associated with anomalous Ekman drifts and are consequently in phase with the changing surface fluxes. Elsewhere changes in surface fluxes dominate over changes in ocean heat transport. These results suggest that almost all of the variability of the North Atlantic SST in the last four decades can be explained as a response to changes in surface fluxes caused by changes in the atmospheric circulation. Changes in the mean atmospheric circulation force the SST while atmospheric eddy fluxes dampen the SST. Both the interannual variability and the longer timescale changes can be explained in this way. While the authors were unable to find evidence for changes in ocean heat transport systematically leading or lagging development of SST anomalies, this leaves open the problem of explaining the causes of the low-frequency variability. Possible causes are discussed with reference to the modeling results.
Abstract
Numerical experiments are performed to examine the causes of variability of Atlantic Ocean SST during the period covered by the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (1958–98). Three ocean models are used. Two are mixed layer models: one with a 75-m-deep mixed layer and the other with a variable depth mixed layer. For both mixed layer models the ocean heat transports are assumed to remain at their diagnosed climatological values. The third model is a full dynamical ocean general circulation model (GCM). All models are coupled to a model of the subcloud atmospheric mixed layer (AML). The AML model computes the air temperature and humidity by balancing surface fluxes, radiative cooling, entrainment at cloud base, advection and eddy heat, and moisture transports. The models are forced with NCEP–NCAR monthly mean winds from 1958 to 1998.
The ocean mixed layer models adequately reproduce the dominant pattern of Atlantic Ocean climate variability in both its spatial pattern and time dependence. This pattern is the familiar tripole of alternating zonal bands of SST anomalies stretching between the subpolar gyre and the subtropics. This SST pattern goes along with a wind pattern that corresponds to the North Atlantic Oscillation (NAO). Analysis of the results reveals that changes in wind speed create the subtropical SST anomalies while at higher latitudes changes in advection of temperature and humidity and changes in atmospheric eddy fluxes are important.
An observational analysis of the boundary layer energy balance is also performed. Anomalous atmospheric eddy heat fluxes are very closely tied to the SST anomalies. Anomalous horizontal eddy fluxes damp the SST anomalies while anomalous vertical eddy fluxes tend to cool the entire midlatitude North Atlantic during the NAO’s high-index phase with the maximum cooling exactly where the SST gradient is strengthened the most.
The SSTs simulated by the ocean mixed layer model are compared with those simulated by the dynamic ocean GCM. In the far North Atlantic Ocean anomalous ocean heat transports are equally important as surface fluxes in generating SST anomalies and they act constructively. The anomalous heat transports are associated with anomalous Ekman drifts and are consequently in phase with the changing surface fluxes. Elsewhere changes in surface fluxes dominate over changes in ocean heat transport. These results suggest that almost all of the variability of the North Atlantic SST in the last four decades can be explained as a response to changes in surface fluxes caused by changes in the atmospheric circulation. Changes in the mean atmospheric circulation force the SST while atmospheric eddy fluxes dampen the SST. Both the interannual variability and the longer timescale changes can be explained in this way. While the authors were unable to find evidence for changes in ocean heat transport systematically leading or lagging development of SST anomalies, this leaves open the problem of explaining the causes of the low-frequency variability. Possible causes are discussed with reference to the modeling results.
Abstract
In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.
Abstract
In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.
