Search Results
You are looking at 1 - 4 of 4 items for
- Author or Editor: C. Deser x
- Refine by Access: All Content x
Abstract
The climate of the eastern Pacific exhibits a pronounced equatorial asymmetry. Boundary layer air originating in the Southern Hemisphere trades crosses the equator and flows into the intertropical convergence zone (ITCZ), whose southern limit is nearly always located at least 4° to the north of the equator. The sea-surface temperature (SST) distribution is characterized by a prominent “cold tongue” centered ∼ 1°S, a strong frontal zone centered ∼ 2°N, and a warm eastward current centered near 5°N. The surface wind field exhibits a pronounced horizontal divergence as the air flows northward across the oceanic frontal zone.
These features vary in strength in response to the annual cycle and the El Niño/Southern Oscillation phenomenon. The northward cross-equatorial surface winds, the cold tongue and the frontal zone all tend to be strongest during the cold season (July through November). During the cold season of the coldest years, when the cold tongue is most prominent, the cross-equatorial flow tends to be weaker than normal and the northward flow across 5°N stronger than normal.
It is shown that within a few degrees of the equator the meridional equation of motion for the surface winds reduces to a balance between the pressure gradient force and the frictional term that involves the vertical derivative of the vertical flux of momentum by subgrid scale processes. Some of the seasonal and interannual variability of the surface winds appears to be a response to the hydrostatic sea-level pressure changes induced by variations in the strength of the cold tongue. However, that the maximum divergence of the surface winds is observed directly above the oceanic frontal zone rather than over the cold tongue appears to be due to the reduction in vertical wind shear within the lowest 100 m that occurs as air parcels pass northward from the cold tongue to the much warmer waters or the North Equatorial Countercurrent. As evidence of the existence of strong vertical wind shear in the stable boundary layer regime over the cold tongue, we note that northward velocities just 100 m above sea level at the Galapagos Islands have been reported to be on the order of 15 m s−1; more than twice as strong as the surface winds.
Abstract
The climate of the eastern Pacific exhibits a pronounced equatorial asymmetry. Boundary layer air originating in the Southern Hemisphere trades crosses the equator and flows into the intertropical convergence zone (ITCZ), whose southern limit is nearly always located at least 4° to the north of the equator. The sea-surface temperature (SST) distribution is characterized by a prominent “cold tongue” centered ∼ 1°S, a strong frontal zone centered ∼ 2°N, and a warm eastward current centered near 5°N. The surface wind field exhibits a pronounced horizontal divergence as the air flows northward across the oceanic frontal zone.
These features vary in strength in response to the annual cycle and the El Niño/Southern Oscillation phenomenon. The northward cross-equatorial surface winds, the cold tongue and the frontal zone all tend to be strongest during the cold season (July through November). During the cold season of the coldest years, when the cold tongue is most prominent, the cross-equatorial flow tends to be weaker than normal and the northward flow across 5°N stronger than normal.
It is shown that within a few degrees of the equator the meridional equation of motion for the surface winds reduces to a balance between the pressure gradient force and the frictional term that involves the vertical derivative of the vertical flux of momentum by subgrid scale processes. Some of the seasonal and interannual variability of the surface winds appears to be a response to the hydrostatic sea-level pressure changes induced by variations in the strength of the cold tongue. However, that the maximum divergence of the surface winds is observed directly above the oceanic frontal zone rather than over the cold tongue appears to be due to the reduction in vertical wind shear within the lowest 100 m that occurs as air parcels pass northward from the cold tongue to the much warmer waters or the North Equatorial Countercurrent. As evidence of the existence of strong vertical wind shear in the stable boundary layer regime over the cold tongue, we note that northward velocities just 100 m above sea level at the Galapagos Islands have been reported to be on the order of 15 m s−1; more than twice as strong as the surface winds.
Abstract
The adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.
Abstract
The adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.
Abstract
Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.
Abstract
Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.
Abstract
While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.
Abstract
While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.
