Search Results
Abstract
Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93.
The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST.
Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.
Abstract
Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93.
The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST.
Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.
Abstract
A hybrid coupled model (HCM) of the tropical ocean–atmosphere system is described. The ocean component is a fully nonlinear ocean general circulation model (OGCM). The atmospheric element is a statistical model that specifies wind stress from ocean-model sea surface temperatures (SST). The coupled model demonstrates a chaotic behavior during extended integration that is related to slow changes in the background mean state of the ocean. The HCM also reproduces many of the observed variations in the tropical Pacific ocean-atmosphere system.
The physical processes operative in the model together describe a natural mode of climate variability in the tropical Pacific ocean–atmosphere system. The mode is composed of (i) westward-propagating Rossby waves and (ii) an equatorially confined air–sea element that propagates eastward. Additional results showed that the seasonal dependence of the anomalous ocean–atmosphere coupling was vital to the model's ability to both replicate and forecast key features of the tropical Pacific climate system.
A series of hindcast and forecast experiments was conducted with the model. It showed real skill in forecasting fall/winter tropical Pacific SST at a lead time of up to 18 months. This skill was largely confined to the central equatorial Pacific, just the region that is most prominent in teleconnections with the Northern Hemisphere during winter. This result suggests the model forecasts of winter SST at leads times of at least 6 months are good enough to be used with atmospheric models (statistical or OGCM) to attempt long-range winter forecasts for the North American continent. This suggestion is confirmed in Part II of this paper.
Abstract
A hybrid coupled model (HCM) of the tropical ocean–atmosphere system is described. The ocean component is a fully nonlinear ocean general circulation model (OGCM). The atmospheric element is a statistical model that specifies wind stress from ocean-model sea surface temperatures (SST). The coupled model demonstrates a chaotic behavior during extended integration that is related to slow changes in the background mean state of the ocean. The HCM also reproduces many of the observed variations in the tropical Pacific ocean-atmosphere system.
The physical processes operative in the model together describe a natural mode of climate variability in the tropical Pacific ocean–atmosphere system. The mode is composed of (i) westward-propagating Rossby waves and (ii) an equatorially confined air–sea element that propagates eastward. Additional results showed that the seasonal dependence of the anomalous ocean–atmosphere coupling was vital to the model's ability to both replicate and forecast key features of the tropical Pacific climate system.
A series of hindcast and forecast experiments was conducted with the model. It showed real skill in forecasting fall/winter tropical Pacific SST at a lead time of up to 18 months. This skill was largely confined to the central equatorial Pacific, just the region that is most prominent in teleconnections with the Northern Hemisphere during winter. This result suggests the model forecasts of winter SST at leads times of at least 6 months are good enough to be used with atmospheric models (statistical or OGCM) to attempt long-range winter forecasts for the North American continent. This suggestion is confirmed in Part II of this paper.
Abstract
This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.
Abstract
This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.
