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I. G. Watterson

Abstract

The structure of eddies forcing the vacillation of the southern midlatitude tropospheric zonal-mean zonal wind and the significance of wave–mean flow feedbacks on its persistence are assessed using a 100-yr 8-h dataset simulated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 general circulation model. Using time-lagged regression and composite analyses relative to the vacillation index, it is shown that high-frequency (HF) eddy momentum flux anomalies near the mean jet latitude provide much of the forcing of the zonal-mean anomalies, as in observations. Low-frequency (LF) eddies also contribute, while the cross-frequency flux enhances short-term variation of the index. The HF band also provides a positive feedback, which is partly countered by an LF negative feedback. High- and low-index composites of representative midtropospheric waves (zonal wavenumbers 7 for HF and 3 for LF) are constructed, including those for waves phase shifted relative to the wave at the jet latitude at each of several lags. Such waves are coherent for only a week, but they provide most of the initial flux anomaly associated with the forcings and the feedbacks. Barotropic wave model simulations suggest that much of the feedback is due to the dependence of wave evolution on the zonal wind states of the composites, although energy variations also contribute. A stochastic model of the momentum equation terms is constructed. This matches the statistics of the index and the momentum terms well. The net feedback more than doubles the 30-day persistence of the index in the annual case. The vacillation index contains significant seasonal variation. Forcing and damping are both weaker in summer, while the negative and positive feedbacks almost negate each other in spring and autumn.

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I. G. Watterson

Abstract

The current generation of climate models, as represented by phase 5 of the Coupled Model Intercomparison Project (CMIP5), has previously been assessed as having more skill in simulating the observed climate than the previous ensemble from phase 3 of CMIP (CMIP3). Furthermore, the skill of models in reproducing seasonal means of precipitation, temperature, and pressure from two observational datasets, quantified by the nondimensional Arcsin–Mielke skill score, appeared to be influenced by model resolution. The analysis is extended to 42 CMIP5 and 24 CMIP3 models. For the combined skill scores for six continents, averaged over the three variables and four seasons, the correlation with model grid length in the 66-model ensemble is −0.73. Focusing on the comparison with ERA-Interim data at higher resolution and with greater regional detail, correlations are nearly as strong for scores over the ocean domain as for land. For the global domain (excluding the Antarctic cap), the correlation of the overall skill score with grid length is −0.61, and it is nearly as strong for each variable. For most tests the improved averaged score of CMIP5 models relative to those from CMIP3 is largely consistent with their increased resolution. However, the improvement for precipitation and the correlations with length are both smaller if rmse is used as a metric. They are smaller again using the GPCP observational data, as the regional detail from a high-resolution model can lead to larger differences when compared to relatively smooth observational fields.

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I. G. Watterson

Abstract

The common representation of two-dimensional nondivergent oceanic currents or fluxes by a streamfunction is extended to the divergent global case using the Helmholtz decomposition of the flow into rotational (nondivergent) and divergent (irrotational) components, represented by stream- and potential functions, respectively. An appropriate discretization of the problem, in either horizontal or vertical planes, is described. While a standard numerical solver of the Poisson equation for the streamfunction in the spherical, horizontal problem does not constrain the rotational flow to the ocean, a simple iterative procedure is used to do this. This involves modifying the coastal vorticity at each step by resetting the flow over land to zero. The scheme is easily programmed and efficiently determines the streamfunction for realistic flows. The potential can then be determined simply.

The decomposition of the time-mean volume fluxes simulated by the Commonwealth Scientific and Industrial Research Organisation ocean general circulation model is considered to illustrate the approach. For the horizontal case, the decomposition of fluxes within the model layers is presented. The gyres within each ocean basin are represented by a streamfunction for each layer, while the potential function represents overturning flows. As an example of the latitude-depth case, the decomposition of the simulated Atlantic sector circulation extends the usual streamfunction into the Southern Ocean. The decomposition of flow in the equatorial longitude-depth plane represents both the westward surface flux and the eastward undercurrent via a streamfunction. A brief comparison of the decomposition with other approaches is made.

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I. G. Watterson

Abstract

Both high-latitude (HLM) and low-latitude modes (LLM) of variability of zonal wind in the Southern Hemisphere have been identified. Through an analysis of a simulation for 1871–2200 by the CSIRO Mark 3 climate model, the extent to which these might both be described as “annular modes,” based on their statistical patterns, physical mechanisms, and usefulness in climate study, is assessed. The modes are determined as EOF1 and EOF2 of vertically integrated zonal and monthly mean zonal wind, for 1871–1970. These match well those from ECMWF Re-Analysis (ERA) data and also from the earlier Mark 2 model. The mode index time series relate to largely annular patterns of local wind and surface pressure anomalies [with HLM giving the familiar southern annular mode (SAM)], and other simulated quantities. While modes calculated from 90° sectors are only moderately correlated (mostly in the polar region) for HLM, the link increases with time scale. There is little such relationship for LLM. A momentum equation analysis using daily data confirms that both zonal modes are driven by eddies, but only HLM features a positive eddy–mean flow feedback. Variation in feedback and surface damping through the seasonal cycle relate well to that in index autocorrelation, with the HLM being more persistent in summer. Stratospheric winds feature a long-lived component that tends to lead the HLM. The HLM drives sea surface temperature anomalies that persist for months, and coupling with the ocean increases variability on longer time scales. The annular variability in the warmer climate of the twenty-second century is barely changed, but the mean climate change in the far south projects strongly on the HLM. The LLM features some statistical annularity and may have some uses. However, only the HLM can be considered to be a physically based mode—the zonal-wind equivalent to the one southern annular mode.

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I. G. Watterson

Abstract

Much of the variance of monthly and zonal mean zonal wind anomalies in the Southern Hemisphere troposphere can be attributed to the vacillation of a barotropic dipole-like “mode.” In four multidecadal simulations of the CSIRO general circulation model, the vacillation is also associated with variations in atmospheric temperature, surface pressure, cloud cover, rainfall, surface, and top-of-atmosphere energy fluxes, surface stresses, and ocean currents. Surface heat fluxes reach 6 W m−2 in composites of months of high value of the vacillation index. In simulations of the model that include either a mixed-layer or general circulation ocean model, these fluxes produce zonal mean SST anomalies, reaching 0.14°C at 45°S, which persist for several months. Ocean dynamics modifies this response, particularly farther south. The atmosphere then responds, although weakly, to these ocean anomalies, as is demonstrated by comparing the interacting-ocean runs with runs where SSTs follow either a climatological annual cycle or observed variations, and also by an additional experiment in which the ocean anomalies are specified. This response clearly alters the evolution of lower-tropospheric temperatures in the months after the index peak. It also leads to a small southward shift in the decaying vacillation wind pattern, as well as a weakly anomalous subtropical jet. The SST anomalies are broadly consistent with a simple model of the air–sea interaction, in which the atmospheric vacillation index is stochastically generated. By adding a second, atmospheric response mode, with specified latitudinal structure, the simple model is also able to reproduce the evolving temperature and wind patterns. It is noted that the GCM vacillation and its interaction with the ocean are broadly consistent with the National Centers for Environmental Prediction observational dataset, within the limitations of the record.

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I. G. Watterson

Abstract

A simple energy balance model with two parameters, an effective heat capacity and an effective climate sensitivity, is used to interpret six GCM simulations of greenhouse gas–induced global warming. By allowing the parameters to vary in time, the model can be accurately calibrated for each run. It is found that the sensitivity can be approximated as a constant in each case. However, the effective heat capacity clearly varies, and it is important that the energy equation is formulated appropriately, and thus unlike many such models. For simulations with linear forcing and from a cold start, the capacity is in each case close to that of a homogeneous ocean with depth initially 200 m, but increasing some 4.3 m each year, irrespective of the sensitivity and forcing growth rate. Analytic solutions for this linear capacity function are derived, and these reproduce the GCM runs well, even for cases where the forcing is stabilized after a century or so. The formation of a subsurface maximum in the mean ocean temperature anomaly is a significant feature of such cases. A simple model for a GCM run with a realistic forcing scenario starting from 1880 is constructed using component results for forcing segments. Given this, an estimate of the cold start error of a simulation of the warming due to forcing after the present would be given by the negative of the temperature drift of the anomaly due to the past forcing. The simple model can evidently be used to give an indication of likely warming curves, at least for this range of scenarios and GCM sensitivities.

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I. G. Watterson

Abstract

An extended coupled atmosphere–ocean model simulation has been analyzed to explore the relationship between Australian rainfall and regional surface temperature anomalies. The interannual variability of seasonal rainfall in the model (which has a weak El Niño) was generally similar to that from a simulation by the atmospheric model with SSTs specified to follow a repeated annual cycle, suggesting that much of the variability is generated internally to the atmosphere. Given the expected role of winds, rotated principal component analyses of both January and July regional 800-hPa wind anomalies from the coupled model were performed. In each season, two wind patterns correlated with realistic rainfall patterns: a monsoon surge pattern brought rainfall to northern Australia in January; a northwesterly flow induced winter rainfall along a band extending to the southeast; and a pattern including northerlies along the east coast brought rainfall to eastern Australia, in both winter and summer. The wind patterns also induced significant surface temperature anomalies over both land and sea. Correlations between the wind-related rainfall indices and seasonal SSTs produced regional SST patterns with much in common to those observed. In particular, the northwesterly rainfall coincided with an eastern Indian Ocean dipole pattern. Aside from the monsoon surge, there is little long-term precursor to these wind anomalies in the model, however, and hence little predictability of the rainfall patterns via the SSTs. Clearly, substantial seasonal rainfall–SST relationships do not necessarily result from forcing of wind anomalies by SSTs, and, excepting El Niño, much of the Australian relationship may, evidently, be unforced.

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I. G. Watterson, J. Bathols, and C. Heady

Climate modeling groups from four continents have submitted simulations as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). With climate impact assessment in mind, we test the accuracy of the seasonal averages of temperature, precipitation, and mean sea level pressure, compared to two observational datasets. Nondimensional skill scores have been generated for the global land and six continental domains. For most cases the 25 models analyzed perform well, particularly the models from Europe. Overall, this CMIP5 ensemble shows improved skill over the earlier (ca. 2005) CMIP3 ensemble of 24 models. This improvement is seen for each variable and continent, and in each case it is largely consistent with the increased resolution on average of CMIP5, given the correlation between scores and grid length found across the combined ensemble. From this apparent influence on skill, the smaller average score for the 13 Earth system models in CMIP5 is consistent with their mostly lower resolution. There is some variation in the ranking of models by skill score for the global, versus continental, measures of skill, and this prompts consideration of the potential influence of a regional focus that model developers might have. While some models rank considerably better in their “home” continent than globally, most have similar ranks in the two domains. Averaging over each ensemble, the home rank is better by only one or two ranks, indicating that the location of development is only a minor influence.

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