Journal articles on the topic 'Atmospheric circulation Australia Mathematical models'
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1
Grose, Michael R., James S. Risbey, Aurel F. Moise, Stacey Osbrough, Craig Heady, Louise Wilson, and Tim Erwin. "Constraints on Southern Australian Rainfall Change Based on Atmospheric Circulation in CMIP5 Simulations." Journal of Climate 30, no. 1 (January 2017): 225–42. http://dx.doi.org/10.1175/jcli-d-16-0142.1.
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Atmospheric circulation change is likely to be the dominant driver of multidecadal rainfall trends in the midlatitudes with climate change this century. This study examines circulation features relevant to southern Australian rainfall in January and July and explores emergent constraints suggested by the intermodel spread and their impact on the resulting rainfall projection in the CMIP5 ensemble. The authors find relationships between models’ bias and projected change for four features in July, each with suggestions for constraining forced change. The features are the strength of the subtropical jet over Australia, the frequency of blocked days in eastern Australia, the longitude of the peak blocking frequency east of Australia, and the latitude of the storm track within the polar front branch of the split jet. Rejecting models where the bias suggests either the direction or magnitude of change in the features is implausible produces a constraint on the projected rainfall reduction for southern Australia. For RCP8.5 by the end of the century the constrained projections are for a reduction of at least 5% in July (with models showing increase or little change being rejected). Rejecting these models in the January projections, with the assumption the bias affects the entire simulation, leads to a rejection of wet and dry outliers.
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Colberg, Frank, Kathleen L. McInnes, Julian O'Grady, and Ron Hoeke. "Atmospheric circulation changes and their impact on extreme sea levels around Australia." Natural Hazards and Earth System Sciences 19, no. 5 (May 21, 2019): 1067–86. http://dx.doi.org/10.5194/nhess-19-1067-2019.
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Abstract. Projections of sea level rise (SLR) will lead to increasing coastal impacts during extreme sea level events globally; however, there is significant uncertainty around short-term coastal sea level variability and the attendant frequency and severity of extreme sea level events. In this study, we investigate drivers of coastal sea level variability (including extremes) around Australia by means of historical conditions as well as future changes under a high greenhouse gas emissions scenario (RCP 8.5). To do this, a multi-decade hindcast simulation is validated against tide gauge data. The role of tide–surge interaction is assessed and found to have negligible effects on storm surge characteristic heights over most of the coastline. For future projections, 20-year-long simulations are carried out over the time periods 1981–1999 and 2081–2099 using atmospheric forcing from four CMIP5 climate models. Changes in extreme sea levels are apparent, but there are large inter-model differences. On the southern mainland coast all models simulated a southward movement of the subtropical ridge which led to a small reduction in sea level extremes in the hydrodynamic simulations. Sea level changes over the Gulf of Carpentaria in the north are largest and positive during austral summer in two out of the four models. In these models, changes to the northwest monsoon appear to be the cause of the sea level response. These simulations highlight a sensitivity of this semi-enclosed gulf to changes in large-scale dynamics in this region and indicate that further assessment of the potential changes to the northwest monsoon in a larger multi-model ensemble should be investigated, together with the northwest monsoon's effect on extreme sea levels.
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Tozer, Carly R., James S. Risbey, Didier P. Monselesan, Dougal T. Squire, Matthew A. Chamberlain, Richard J. Matear, and Tilo Ziehn. "Assessing the Representation of Australian Regional Climate Extremes and Their Associated Atmospheric Circulation in Climate Models." Journal of Climate 33, no. 4 (February 15, 2020): 1227–45. http://dx.doi.org/10.1175/jcli-d-19-0287.1.
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AbstractWe assess the representation of multiday temperature and rainfall extremes in southeast Australia in three coupled general circulation models (GCMs) of varying resolution. We evaluate the statistics of the modeled extremes in terms of their frequency, duration, and magnitude compared to observations, and the model representation of the midtropospheric circulation (synoptic and large scale) associated with the extremes. We find that the models capture the statistics of observed heatwaves reasonably well, though some models are “too wet” to adequately capture the observed duration of dry spells but not always wet enough to capture the magnitude of extreme wet events. Despite the inability of the models to simulate all extreme event statistics, the process evaluation indicates that the onset and decay of the observed synoptic structures are well simulated in the models, including for wet and dry extremes. We also show that the large-scale wave train structures associated with the observed extremes are reasonably well simulated by the models although their broader onset and decay is not always captured in the models. The results presented here provide some context for, and confidence in, the use of the coupled GCMs in climate prediction and projection studies for regional extremes.
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Cai, Wenju, Tim Cowan, Arnold Sullivan, Joachim Ribbe, and Ge Shi. "Are Anthropogenic Aerosols Responsible for the Northwest Australia Summer Rainfall Increase? A CMIP3 Perspective and Implications." Journal of Climate 24, no. 10 (May 15, 2011): 2556–64. http://dx.doi.org/10.1175/2010jcli3832.1.
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Abstract Severe rainfall deficiencies have plagued southern and eastern Australian regions over the past decades, where the long-term rainfall is projected to decrease. By contrast, there has been an increase over northwest Australia (NWA) in austral summer, which, if it continues, could be an important future water resource. If increasing anthropogenic aerosols contribute to the observed increase in summer rainfall, then, as anthropogenic aerosols are projected to decrease, what will the likely impact over NWA be? This study uses output from 24 climate models submitted to phase 3 of the Coupled Model Intercomparison Project (CMIP3) with a total of 75 experiments to provide a multimodel perspective. The authors find that none of the ensemble averages, either with both the direct and indirect anthropogenic aerosol effect (10 models, 32 experiments) or with the direct effect only (14 models, 43 experiments), simulate the observed NWA rainfall increase. Given this, it follows that a projected rainfall reduction is not due to a projected decline in future aerosol concentrations. The authors show that the projected NWA rainfall reduction is associated with an unrealistic and overly strong NWA rainfall teleconnection with the El Niño–Southern Oscillation (ENSO). The unrealistic teleconnection is primarily caused by a model equatorial Pacific cold tongue that extends too far into the western Pacific, with the ascending branch of the Walker circulation situated too far west, exerting an influence on rainfall over NWA rather than over northeast Australia. Models with a greater present-day ENSO amplitude produce a greater reduction in the Walker circulation and hence a greater reduction in NWA rainfall in a warming climate. Hence, the cold bias and its impact represent a source of uncertainty for climate projections.
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Johnson, Fiona, and Ashish Sharma. "A Comparison of Australian Open Water Body Evaporation Trends for Current and Future Climates Estimated from Class A Evaporation Pans and General Circulation Models." Journal of Hydrometeorology 11, no. 1 (February 1, 2010): 105–21. http://dx.doi.org/10.1175/2009jhm1158.1.
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Abstract Trends of decreasing pan evaporation around the world have renewed interest in evaporation and its behavior in a warming world. Observed pan evaporation around Australia has been modeled to attribute changes in its constituent variables. It is found that wind speed decreases have generally led to decreases in pan evaporation. Trends were also calculated from reanalysis and general circulation model (GCM) outputs. The reanalysis reflected the general pattern and magnitude of the observed station trends across Australia. However, unlike the station trends, the reanalysis trends are mainly driven by vapor pressure deficit changes than wind speed changes. Some of the GCMs modeled the trends well, but most showed an average positive trend for Australia. Half the GCMs analyzed show increasing wind speed trends, and most show larger changes in vapor pressure deficit than would be expected based on the station data. Future changes to open water body evaporation have also been assessed using projections for two emission scenarios. Averaged across Australia, the models show a 5% increase in open water body evaporation by 2070 compared to 1990 levels. There is considerable variability in the model projections, particularly for the aerodynamic component of evaporation. Assumptions of increases in evaporation in a warming world need to be considered in light of the variability in the parameters that affect evaporation.
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Bates, Bryson C., Andrew J. Dowdy, and Richard E. Chandler. "Lightning Prediction for Australia Using Multivariate Analyses of Large-Scale Atmospheric Variables." Journal of Applied Meteorology and Climatology 57, no. 3 (March 2018): 525–34. http://dx.doi.org/10.1175/jamc-d-17-0214.1.
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AbstractLightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004–13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones.
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Horenko, Illia. "On Robust Estimation of Low-Frequency Variability Trends in Discrete Markovian Sequences of Atmospheric Circulation Patterns." Journal of the Atmospheric Sciences 66, no. 7 (July 1, 2009): 2059–72. http://dx.doi.org/10.1175/2008jas2959.1.
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Abstract Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequence of atmospheric circulation patterns over the United Kingdom between 1946 and 2007. It is demonstrated that the influence of the seasonal pattern variability on transition processes is dominated by the long-term effects revealed by the introduced methods. Despite the differences in the mathematical assumptions implied by both presented methods, almost identical symmetrical changes of the cyclonic and anticyclonic pattern probabilities are identified in the analyzed data, with the confidence intervals being smaller than in the case of the local Gaussian kernel smoothing algorithm. Analysis results are investigated with respect to model sensitivity and compared to a standard analysis technique based on a local Gaussian kernel smoothing. Finally, the implications of the discussed strategies on long-range predictability of the data-fitted Markovian models are discussed.
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Gibson, Peter B., Andrew J. Pitman, Ruth Lorenz, and Sarah E. Perkins-Kirkpatrick. "The Role of Circulation and Land Surface Conditions in Current and Future Australian Heat Waves." Journal of Climate 30, no. 24 (December 2017): 9933–48. http://dx.doi.org/10.1175/jcli-d-17-0265.1.
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Understanding the physical drivers of heat waves is essential for improving short-term forecasts of individual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalysis data. In particular, a several-fold increase in the likelihood of heat wave day occurrence is found during days of reduced evaporative fraction under favorable circulation conditions. The atmospheric circulation and land surface conditions linked to heat waves in reanalysis were then examined in the context of CMIP5 climate model projections. Large uncertainty was found to exist for many regions, especially in terms of the direction of future land surface changes and in terms of the magnitude of atmospheric circulation changes. Efforts to constrain uncertainty in both atmospheric and land surface processes in climate models, while challenging, should translate to more robust regional projections of heat waves.
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Charles, S. P., B. C. Bates, and N. R. Viney. "Linking atmospheric circulation to daily rainfall patterns across the Murrumbidgee River Basin." Water Science and Technology 48, no. 7 (October 1, 2003): 233–40. http://dx.doi.org/10.2166/wst.2003.0445.
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The hydrological cycle in Australia covers an extraordinary range of climatic and hydrologic regimes. It is now widely accepted that Australian hydrology is significantly different from all other regions and continents with the partial exception of southern Africa. Rainfall variability is very high in almost all regions with respect to amount and the lengths of wet and dry spells. These factors are keys to the behaviour and health of Australian aquatic ecosystems and water resources. Thus assessment of how rainfall may change under a potential future climate is critical. For a case study of the Murrumbidgee River Basin (MRB), a statistical downscaling model that links broad scale atmospheric circulation to multi-site, daily precipitation is assessed using observed data. This model can be driven with climate model simulations to produce rainfall scenarios at the scale required by impacts models. These can then be used in probabilistic risk assessments of climate change impacts on river health. These issues will be discussed in the context of assessing the potential impacts of precipitation changes due to projected climate change on river health.
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Spinoni, Jonathan, Paulo Barbosa, Edoardo Bucchignani, John Cassano, Tereza Cavazos, Jens H. Christensen, Ole B. Christensen, et al. "Future Global Meteorological Drought Hot Spots: A Study Based on CORDEX Data." Journal of Climate 33, no. 9 (May 1, 2020): 3635–61. http://dx.doi.org/10.1175/jcli-d-19-0084.1.
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AbstractTwo questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.44°) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only, ~15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (~47% under RCP4.5, ~49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas.
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Bergner, Nora, Marina Friedel, Daniela I. V. Domeisen, Darryn Waugh, and Gabriel Chiodo. "Exploring the link between austral stratospheric polar vortex anomalies and surface climate in chemistry-climate models." Atmospheric Chemistry and Physics 22, no. 21 (November 1, 2022): 13915–34. http://dx.doi.org/10.5194/acp-22-13915-2022.
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Abstract. Extreme events in the stratospheric polar vortex can lead to changes in the tropospheric circulation and impact the surface climate on a wide range of timescales. The austral stratospheric vortex shows its largest variability in spring, and a weakened polar vortex is associated with changes in the spring to summer surface climate, including hot and dry extremes in Australia. However, the robustness and extent of the connection between polar vortex strength and surface climate on interannual timescales remain unclear. We assess this relationship by using reanalysis data and time-slice simulations from two chemistry-climate models (CCMs), building on previous work that is mainly based on observations. The CCMs show a similar downward propagation of anomalies in the polar vortex strength to the reanalysis data: a weak polar vortex is on average followed by a negative tropospheric Southern Annular Mode (SAM) in spring to summer, while a strong polar vortex is on average followed by a positive SAM. The signature in the surface climate following polar vortex weakenings is characterized by high surface pressure and warm temperature anomalies over Antarctica, the region where surface signals are most robust across all model and observational datasets. However, the tropospheric SAM response in the two CCMs considered is inconsistent with observations. In one CCM, the SAM is more negative compared to the reanalysis after weak polar vortex events, whereas in the other CCM, it is less negative. In addition, neither model reproduces all the regional changes in midlatitudes, such as the warm and dry anomalies over Australia. We find that these inconsistencies are linked to model biases in the basic state, such as the latitude of the eddy-driven jet and the persistence of the SAM. These results are largely corroborated by models that participated in the Chemistry-Climate Model Initiative (CCMI). Furthermore, bootstrapping of the data reveals sizable uncertainty in the magnitude of the surface signals in both models and observations due to internal variability. Our results demonstrate that anomalies of the austral stratospheric vortex have significant impacts on surface climate, although the ability of models to capture regional effects across the Southern Hemisphere is limited by biases in their representation of the stratospheric and tropospheric circulation.
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Schepen, Andrew, and Q. J. Wang. "Toward Accurate and Reliable Forecasts of Australian Seasonal Rainfall by Calibrating and Merging Multiple Coupled GCMs." Monthly Weather Review 141, no. 12 (November 25, 2013): 4554–63. http://dx.doi.org/10.1175/mwr-d-12-00253.1.
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Abstract The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.
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Philbin, R., and M. Jun. "Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies." Advances in Statistical Climatology, Meteorology and Oceanography 1, no. 1 (May 22, 2015): 29–44. http://dx.doi.org/10.5194/ascmo-1-29-2015.
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Abstract. This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.
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Panja, D., and F. M. Selten. "Extreme associated functions: optimally linking local extremes to large-scale atmospheric circulation structures." Atmospheric Chemistry and Physics Discussions 7, no. 5 (October 10, 2007): 14433–60. http://dx.doi.org/10.5194/acpd-7-14433-2007.
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Abstract. We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July–August daily mean temperature at 2 m height (T2m) time-series over the Netherlands and 500 hPa geopotential height (Z500) time-series over the Euroatlantic region of the ECMWF reanalysis dataset (ERA40). The method identifies patterns in the Z500 time-series that optimally describe, in a precise mathematical sense, the relationship with local warm extremes in the Netherlands. Two patterns are identified; the most important one corresponds to a blocking high pressure system leading to subsidence and calm, dry and sunny conditions over the Netherlands. The second one corresponds to a rare, easterly flow regime bringing warm, dry air into the region. The patterns are robust; they are also identified in shorter subsamples of the total dataset. The method is generally applicable and might prove useful in evaluating the performance of climate models in simulating local weather extremes.
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Toniazzo, Thomas, Mats Bentsen, Cheryl Craig, Brian E. Eaton, Jim Edwards, Steve Goldhaber, Christiane Jablonowski, and Peter H. Lauritzen. "Enforcing conservation of axial angular momentum in the atmospheric general circulation model CAM6." Geoscientific Model Development 13, no. 2 (February 21, 2020): 685–705. http://dx.doi.org/10.5194/gmd-13-685-2020.
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Abstract. Numerical general circulation models of the atmosphere are generally required to conserve mass and energy for their application to climate studies. Here we draw attention to another conserved global integral, viz. the component of angular momentum (AM) along the Earth's axis of rotation, which tends to receive less consideration. We demonstrate the importance of global AM conservation in climate simulations with the example of the Community Atmosphere Model (CAM) with the finite-volume (FV) dynamical core, which produces a noticeable numerical sink of AM. We use a combination of mathematical analysis and numerical diagnostics to pinpoint the main source of AM non-conservation in CAM–FV. We then present a method to enforce global conservation of AM, and we discuss the results in a hierarchy of numerical simulations of the atmosphere of increasing complexity. In line with theoretical expectations, we show that even a crude, non-local enforcement of AM conservation in the simulations consistently results in the mitigation of certain persistent model biases.
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Timbal, B., R. Kounkou, and G. A. Mills. "Changes in the Risk of Cool-Season Tornadoes over Southern Australia due to Model Projections of Anthropogenic Warming." Journal of Climate 23, no. 9 (May 1, 2010): 2440–49. http://dx.doi.org/10.1175/2009jcli3456.1.
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Abstract Anthropogenic climate change is likely to be felt most acutely through changes in the frequency of extreme meteorological events. However, quantifying the impact of climate change on these events is a challenge because the core of the climate change science relies on general circulation models to detail future climate projections, and many of these extreme events occur on small scales that are not resolved by climate models. This note describes an attempt to infer the impact of climate change on one particular type of extreme meteorological event—the cool-season tornadoes of southern Australia. The Australian Bureau of Meteorology predicts threat areas for cool-season tornadoes using fine-resolution numerical weather prediction model output to define areas where the buoyancy of a near-surface air parcel and the vertical wind shear each exceed specified thresholds. The diagnostic has been successfully adapted to coarser-resolution climate models and applied to simulations of the current climate, as well as future projections of the climate over southern Australia. Simulations of the late twentieth century are used to validate the models’ ability to reproduce the climatology of the risk of cool-season tornado formation by comparing these with similar computations based on historical reanalyses. Model biases are overcome by setting model specific thresholds to define the cool-season tornado risk. The diagnostic, applied to simulations of the twenty-first century, is then used to quantify the impact of the projected climate change on cool-season tornado risk. The sign of the response is consistent across all models: a decrease of the risk of formation during the twenty-first century is projected, driven by the thermodynamical response. The thermal response is modulated by the dynamical response, which varies between models. The projected decrease in tornadoes risk during the cool season is consistent with the projection of positive southern annular mode trends and the known influence of this mode of variability on interannual to intraseasonal time-scale variations in cool-season tornado occurrence.
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Charles, Andrew, Bertrand Timbal, Elodie Fernandez, and Harry Hendon. "Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin." Monthly Weather Review 141, no. 3 (March 1, 2013): 1099–117. http://dx.doi.org/10.1175/mwr-d-12-00098.1.
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Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.
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Li, Na, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos. "Interannual global carbon cycle variations linked to atmospheric circulation variability." Earth System Dynamics 13, no. 4 (November 7, 2022): 1505–33. http://dx.doi.org/10.5194/esd-13-1505-2022.
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Abstract. One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p≥800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n<60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño–Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30–54 years. For shorter time series (n<30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices.
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Thatcher, Marcus, and John L. McGregor. "A Technique for Dynamically Downscaling Daily-Averaged GCM Datasets Using the Conformal Cubic Atmospheric Model." Monthly Weather Review 139, no. 1 (January 1, 2011): 79–95. http://dx.doi.org/10.1175/2010mwr3351.1.
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Abstract In this paper the authors dynamically downscale daily-averaged general circulation model (GCM) datasets over Australia using the Conformal Cubic Atmospheric Model (CCAM). The technique can take advantage of the wider range of Coupled Model Intercomparison Project phase 3 (CMIP3) daily-averaged GCM datasets than is available using 3-hourly datasets. The daily-averaged host GCM atmospheric data are fitted to a time interpolation formula and then differentiated in time to produce a first-order estimate of the atmosphere at 0000 UTC on each simulation day. The processed GCM data are forced into CCAM using a scale-selective filter with an 18° radius. Since this procedure is unable to account for the diurnal cycle, the forcing data are only applied to winds and air temperatures once per day between 800 and 100 hPa. Lateral boundary conditions are not required since CCAM employs a variable-resolution global grid. The technique is evaluated by downscaling daily-averaged 2.5° NCEP reanalyses over Australia at 60-km resolution from 1971 to 2000 and comparing the results to downscaling the 6-hourly reanalyses and to simulating with sea surface temperature (SST)-only forcing. The results show that the daily-averaged downscaling technique can simulate average seasonal maximum and minimum screen temperatures and rainfall similar to those obtained downscaling 6-hourly reanalyses. Some implications for regional climate projections are considered by downscaling four daily-averaged GCM datasets from the twentieth-century climate in coupled models (20C3M) experiment over Australia.
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Johnson, Fiona, and Ashish Sharma. "Measurement of GCM Skill in Predicting Variables Relevant for Hydroclimatological Assessments." Journal of Climate 22, no. 16 (August 15, 2009): 4373–82. http://dx.doi.org/10.1175/2009jcli2681.1.
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Abstract Simulations from general circulation models are now being used for a variety of studies and purposes. With up to 23 different GCMs now available, it is desirable to determine whether a specific variable from a particular model is representative of the ensemble mean, which is often assumed to indicate the likely state of that variable in the future. The answers are important for decision makers and researchers using selective model outputs for follow-on studies such as statistical downscaling, which currently assume all model outputs are simulated with equal reliability. A skill score, termed the variable convergence score (VCS), has been derived that can be used to rank variables based on the coefficient of variation of the ensemble. The key benefit is the development of a simple methodology that allows for a quantitative assessment between different hydroclimatic variables. The VCS methodology has been applied to the outputs of nine GCMs for eight different variables and two emission scenarios to provide a relative ranking of the variables averaged across Australia and over different climatic regions of the country. The methodology, however, would be applicable for any region or any variable of interest from GCMs. It was found that the surface variables with the highest scores are pressure, temperature, and humidity. Regionally in Australia, models again show the best agreement in the surface pressure projections. The tropical and southwestern temperate zones show the overall highest variable convergence when all variables are considered. The desert zone shows relatively low model agreement, particularly in the projections of precipitation and specific humidity.
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Sun, Chaojiao, Ming Feng, Richard J. Matear, Matthew A. Chamberlain, Peter Craig, Ken R. Ridgway, and Andreas Schiller. "Marine Downscaling of a Future Climate Scenario for Australian Boundary Currents." Journal of Climate 25, no. 8 (April 10, 2012): 2947–62. http://dx.doi.org/10.1175/jcli-d-11-00159.1.
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Abstract Ocean boundary currents are poorly represented in existing coupled climate models, partly because of their insufficient resolution to resolve narrow jets. Therefore, there is limited confidence in the simulated response of boundary currents to climate change by climate models. To address this issue, the eddy-resolving Ocean Forecasting Australia Model (OFAM) was used, forced with bias-corrected output in the 2060s under the Special Report on Emissions Scenarios (SRES) A1B from the CSIRO Mark version 3.5 (Mk3.5) climate model, to provide downscaled regional ocean projections. CSIRO Mk3.5 captures a number of robust changes that are common to most climate models that are consistent with observed changes, including the weakening of the equatorial Pacific zonal wind stress and the strengthening of the wind stress curl in the Southern Pacific, important for driving the boundary currents around Australia. The 1990s climate is downscaled using air–sea fluxes from the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). The current speed, seasonality, and volume transports of the Australian boundary currents show much greater fidelity to the observations in the downscaled model. Between the 1990s and the 2060s, the downscaling with the OFAM simulates a 15% reduction in the Leeuwin Current (LC) transport, a 20% decrease in the Indonesian Throughflow (ITF) transport, a 12% increase in the East Australian Current (EAC) core transport, and a 35% increase in the EAC extension. The projected changes by the downscaling model are consistent with observed trends over the past several decades and with changes in wind-driven circulation derived from Sverdrup dynamics. Although the direction of change projected from downscaling is usually in agreement with CSIRO Mk3.5, there are important regional details and differences that will impact the response of ecosystems to climate change.
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Hetzel, Yasha, Ivica Janekovic, and Charitha Pattiaratchi. "BENEFITS AND LIMITATIONS OF A COUPLED WAVE-SURGE MODEL FOR AUSTRALIAN EXTREMES." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 49. http://dx.doi.org/10.9753/icce.v36v.waves.49.
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Extreme sea levels result from a combination of a range of factors that include long term mean sea level variability, astronomical tides, storm surges due to atmospheric pressure and wind, wave breaking, and other regional dynamics. Numerical circulation/storm-surge models are frequently used to predict water levels over broad areas with the outputs used for planning or emergency management applications. Recently, coupled wave-circulation models have been shown to improve extreme sea level predictions through the inclusion of wave setup that results from the transfer of momentum of breaking waves into sea level at the shoreline. Other studies have shown that the representations of surface wind drag can be improved when the sea state is considered, and this can directly influence the amplitude of storm surges at the coast. However, most coupled wave-circulation model studies have been undertaken for relatively small computational domains and for a limited range of coastal morphologies and storm types. In this paper we assess the benefits and limitations of using a coupled wave-circulation model to predict extreme sea levels and determine wave effects for a broad range of coastal morphologies and extreme storm events all around Australia. Simulated events occurred in three oceans and considered tropical cyclones, a cyclone undergoing extratropical transition, and a large mid-latitude extratropical low-pressure system.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/UfyWHI4OHBA
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Eden, Jonathan M., and Martin Widmann. "Downscaling of GCM-Simulated Precipitation Using Model Output Statistics." Journal of Climate 27, no. 1 (January 1, 2014): 312–24. http://dx.doi.org/10.1175/jcli-d-13-00063.1.
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Abstract Producing reliable estimates of changes in precipitation at local and regional scales remains an important challenge in climate science. Statistical downscaling methods are often utilized to bridge the gap between the coarse resolution of general circulation models (GCMs) and the higher resolutions at which information is required by end users. As the skill of GCM precipitation, particularly in simulating temporal variability, is not fully understood, statistical downscaling typically adopts a perfect prognosis (PP) approach in which high-resolution precipitation projections are based on real-world statistical relationships between large-scale atmospheric predictors and local-scale precipitation. Using a nudged simulation of the ECHAM5 GCM, in which the large-scale weather states are forced toward observations of large-scale circulation and temperature for the period 1958–2001, previous work has shown ECHAM5 skill in simulating temporal variability of precipitation to be high in many parts of the world. Here, the same nudged simulation is used in an alternative downscaling approach, based on model output statistics (MOS), in which statistical corrections are derived for simulated precipitation. Cross-validated MOS corrections based on maximum covariance analysis (MCA) and principal component regression (PCR), in addition to a simple local scaling, are shown to perform strongly throughout much of the extratropics. Correlation between downscaled and observed monthly-mean precipitation is as high as 0.8–0.9 in many parts of Europe, North America, and Australia. For these regions, MOS clearly outperforms PP methods that use temperature and circulation as predictors. The strong performance of MOS makes such an approach to downscaling attractive and potentially applicable to climate change simulations.
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Mao, Yiwen, and Adam Monahan. "Comparison of Linear Predictability of Surface Wind Components from Observations with Simulations from RCMs and Reanalysis." Journal of Applied Meteorology and Climatology 57, no. 4 (April 2018): 889–906. http://dx.doi.org/10.1175/jamc-d-17-0283.1.
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AbstractThis study compares the predictability of surface wind components by linear statistical downscaling using data from both observations and comprehensive models [regional climate models (RCM) and NCEP-2 reanalysis] in three domains: North America (NAM), Europe–Mediterranean Basin (EMB), and East Asia (EAS). A particular emphasis is placed on predictive anisotropy, a phenomenon referring to unequal predictability of surface wind components in different directions. Simulated predictability by comprehensive models is generally close to that found in observations in flat regions of NAM and EMB, but it is overestimated relative to observations in mountainous terrain. Simulated predictability in EAS shows different structures. In particular, there are regions in EAS where predictability simulated by RCMs is lower than that in observations. Overestimation of predictability by comprehensive models tends to occur in regions of low predictability in observations and can be attributed to small-scale physical processes not resolved by comprehensive models. An idealized mathematical model is used to characterize the predictability of wind components. It is found that the signal strength along the direction of minimum predictability is the dominant control on the strength of predictive anisotropy. The biases in the model representation of the statistical relationship between free-tropospheric circulation and surface winds are interpreted in terms of inadequate simulation of small-scale processes in regional and global models, and the primary cause of predictive anisotropy is attributed to such small-scale processes.
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Dorrestijn, Jesse, Daan T. Crommelin, A. Pier Siebesma, Harmen J. J. Jonker, and Christian Jakob. "Stochastic Parameterization of Convective Area Fractions with a Multicloud Model Inferred from Observational Data." Journal of the Atmospheric Sciences 72, no. 2 (February 1, 2015): 854–69. http://dx.doi.org/10.1175/jas-d-14-0110.1.
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Abstract Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convection parameterization schemes used in general circulation models.
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Li, Chen, Jing-Jia Luo, Shuanglin Li, Harry Hendon, Oscar Alves, and Craig MacLachlan. "Multimodel Prediction Skills of the Somali and Maritime Continent Cross-Equatorial Flows." Journal of Climate 31, no. 6 (March 2018): 2445–64. http://dx.doi.org/10.1175/jcli-d-17-0272.1.
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Predictive skills of the Somali cross-equatorial flow (CEF) and the Maritime Continent (MC) CEF during boreal summer are assessed using three ensemble seasonal forecasting systems, including the coarse-resolution Predictive Ocean Atmospheric Model for Australia (POAMA, version 2), the intermediate-resolution Scale Interaction Experiment–Frontier Research Center for Global Change (SINTEX-F), and the high-resolution seasonal prediction version of the Australian Community Climate and Earth System Simulator (ACCESS-S1) model. Retrospective prediction results suggest that prediction of the Somali CEF is more challenging than that of the MC CEF. While both the individual models and the multimodel ensemble (MME) mean show useful skill (with the anomaly correlation coefficient being above 0.5) in predicting the MC CEF up to 5-month lead, only ACCESS-S1 and the MME can skillfully predict the Somali CEF up to 2-month lead. Encouragingly, the CEF seesaw index (defined as the difference of the two CEFs as a measure of the negative phase relation between them) can be skillfully predicted up to 4–5 months ahead by SINTEX-F, ACCESS-S1, and the MME. Among the three models, the high-resolution ACCESS-S1 model generally shows the highest skill in predicting the individual CEFs, the CEF seesaw, as well as the CEF seesaw index–related precipitation anomaly pattern in Asia and northern Australia. Consistent with the strong influence of ENSO on the CEFs, the skill in predicting the CEFs depends on the model’s ability in predicting not only the eastern Pacific SST anomaly but also the anomalous Walker circulation that brings ENSO’s influence to bear on the CEFs.
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Andrys, Julia, Thomas J. Lyons, and Jatin Kala. "Multidecadal Evaluation of WRF Downscaling Capabilities over Western Australia in Simulating Rainfall and Temperature Extremes." Journal of Applied Meteorology and Climatology 54, no. 2 (February 2015): 370–94. http://dx.doi.org/10.1175/jamc-d-14-0212.1.
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AbstractThe authors evaluate a 30-yr (1981–2010) Weather Research and Forecast (WRF) Model regional climate simulation over the southwest of Western Australia (SWWA), a region with a Mediterranean climate, using ERA-Interim boundary conditions. The analysis assesses the spatial and temporal characteristics of climate extremes, using a selection of climate indices, with an emphasis on metrics that are relevant for forestry and agricultural applications. Two nested domains at 10- and 5-km resolution are examined, with the higher-resolution simulation resolving convection explicitly. Simulation results are compared with a high-resolution, gridded observational dataset that provides daily rainfall, minimum temperatures, and maximum temperatures. Results show that, at both resolutions, the model is able to simulate the daily, seasonal, and annual variation of temperature and precipitation well, including extreme events. The higher-resolution domain displayed significant performance gains in simulating dry-season convective precipitation, rainfall around complex terrain, and the spatial distribution of frost conditions. The high-resolution domain was, however, influenced by grid-edge effects in the southwestern margin, which reduced the ability of the domain to represent frontal rainfall along the coastal region. On the basis of these results, the authors feel confident in using the WRF Model for regional climate simulations for the SWWA, including studies that focus on the spatial and temporal representation of climate extremes. This study provides a baseline climatological description at a high resolution that can be used for impact studies and will also provide a benchmark for climate simulations driven by general circulation models.
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Maher, Penelope, and Steven C. Sherwood. "Disentangling the Multiple Sources of Large-Scale Variability in Australian Wintertime Precipitation." Journal of Climate 27, no. 17 (August 28, 2014): 6377–92. http://dx.doi.org/10.1175/jcli-d-13-00659.1.
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Abstract Precipitation is influenced by multiple large-scale natural processes. Many of these large-scale precipitation “drivers” are not independent of one another, which complicates attribution. Moreover, it is unclear whether natural interannual drivers alone can explain the observed longer-term precipitation trends or account for projected precipitation changes with global warming seen in climate models. Separating the main interannual drivers from processes that may prevail on longer time scales, such as a poleward circulation shift or increased specific humidity, is essential for an improved understanding of precipitation variability and for making longer-term predictions. In this study, an objective approach to disentangle multiple sources of large-scale variability is applied to Australian precipitation. This approach uses a multivariate linear independence model, involving multiple linear regressions to produce a partial correlation matrix, which directly links variables using significance thresholds to avoid overfitting. This is applied to regional winter precipitation in Australia as a test case, using the ECMWF Interim Re-Analysis (ERA-Interim) and Australian Water Availability Project datasets. Traditional drivers and several drivers associated with the width of the tropics are assessed. The results show that the web of interactions implied by correlations can be simplified using this multivariate linear independence model approach: the total number of apparent precipitation drivers was reduced in each region studied, compared to correlations meeting the same statistical significance. Results show that the edge of the tropics directly influences regional precipitation in Australia and also has an indirect influence, through the interaction of the subtropical ridge and atmospheric blocking. These results provide observational evidence that changes associated with an expansion of the tropics reduce precipitation in subtropical Australia.
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Soldatenko, Sergei A., and Rafael M. Yusupov. "The Determination of Feasible Control Variables for Geoengineering and Weather Modification Based on the Theory of Sensitivity in Dynamical Systems." Journal of Control Science and Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1547462.
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Geophysical cybernetics allows for exploring weather and climate modification (geoengineering) as an optimal control problem in which the Earth’s climate system is considered as a control system and the role of controller is given to human operators. In mathematical models used in climate studies control actions that manipulate the weather and climate can be expressed via variations in model parameters that act as controls. In this paper, we propose the “instability-sensitivity” approach that allows for determining feasible control variables in geoengineering. The method is based on the sensitivity analysis of mathematical models that describe various types of natural instability phenomena. The applicability of this technique is illustrated by a model of atmospheric baroclinic instability since this physical mechanism plays a significant role in the general circulation of the atmosphere and, consequently, in climate formation. The growth rate of baroclinic unstable waves is taken as an indicator of control manipulations. The information obtained via calculated sensitivity coefficients is very beneficial for assessing the physical feasibility of methods of control of the large-scale atmospheric dynamics and for designing optimal control systems for climatic processes. It also provides insight into potential future changes in baroclinic waves, as a result of a changing climate.
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Capotondi, Antonietta, and Michael A. Alexander. "Relationship between Precipitation in the Great Plains of the United States and Global SSTs: Insights from the IPCC AR4 Models." Journal of Climate 23, no. 11 (June 1, 2010): 2941–58. http://dx.doi.org/10.1175/2009jcli3291.1.
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Abstract Multicentury preindustrial control simulations from six of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) models are used to examine the relationship between low-frequency precipitation variations in the Great Plains (GP) region of the United States and global sea surface temperatures (SSTs). This study builds on previous work performed with atmospheric models forced by observed SSTs during the twentieth century and extends it to a coupled model context and longer time series. The climate models used in this study reproduce the precipitation climatology over the United States reasonably well, with maximum precipitation occurring in early summer, as observed. The modeled precipitation time series exhibit negative “decadal” anomalies, identified using a 5-yr running mean, of amplitude comparable to that of the twentieth-century droughts. It is found that low-frequency anomalies over the GP are part of a large-scale pattern of precipitation variations, characterized by anomalies of the same sign as in the GP region over Europe and southern South America and anomalies of opposite sign over northern South America, India, and Australia. The large-scale pattern of the precipitation anomalies is associated with global-scale atmospheric circulation changes; during wet periods in the GP, geopotential heights are raised in the tropics and high latitudes and lowered in the midlatitudes in most models, with the midlatitude jets displaced toward the equator in both hemispheres. Statistically significant correlations are found between the decadal precipitation anomalies in the GP region and tropical Pacific SSTs in all the models. The influence of other oceans (Indian and tropical and North Atlantic), which previous studies have identified as potentially important, appears to be model dependent.
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Potgieter, A. B., G. L. Hammer, H. Meinke, R. C. Stone, and L. Goddard. "Three Putative Types of El Niño Revealed by Spatial Variability in Impact on Australian Wheat Yield." Journal of Climate 18, no. 10 (May 15, 2005): 1566–74. http://dx.doi.org/10.1175/jcli3349.1.
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Abstract The El Niño–Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Niño events are often associated with severe drought conditions. However, El Niño events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Niño are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (“footprints”) found in the El Niño impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean–atmosphere dynamics identifies three types of El Niño differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Niño events on agricultural systems.
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Schepen, Andrew, Q. J. Wang, and David E. Robertson. "Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs." Monthly Weather Review 142, no. 5 (April 30, 2014): 1758–70. http://dx.doi.org/10.1175/mwr-d-13-00248.1.
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Abstract Coupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities derived from ensemble members are often statistically unreliable. Hence, it is necessary to postprocess GCM forecasts to improve skill and statistical reliability. In this study, the authors compare three methods of statistically postprocessing GCM output—calibration, bridging, and a combination of calibration and bridging—as ways to treat these problems and make use of multiple GCM outputs to increase the skill of Australian seasonal rainfall forecasts. Three calibration models are established using ensemble mean rainfall from three variants of the Predictive Ocean Atmosphere Model for Australia (POAMA) version M2.4 as predictors. Six bridging models are established using POAMA forecasts of seasonal climate indices as predictors. The calibration and bridging forecasts are merged through Bayesian model averaging. Forecast attributes including skill, sharpness, and reliability are assessed through a rigorous leave-three-years-out cross-validation procedure for forecasts of 1-month lead time. While there are overlaps in skill, there are regions and seasons where the calibration or bridging forecasts are uniquely skillful. The calibration forecasts are more skillful for January–March (JFM) to June–August (JJA). The bridging forecasts are more skillful for July–September (JAS) to December–February (DJF). Merging calibration and bridging forecasts retains, and in some seasons expands, the spatial coverage of positive skill achieved by the better of the calibration forecasts and bridging forecasts individually. The statistically postprocessed forecasts show improved reliability compared to the raw forecasts.
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Jensen, Michael P., Andrew M. Vogelmann, William D. Collins, Guang J. Zhang, and Edward P. Luke. "Investigation of Regional and Seasonal Variations in Marine Boundary Layer Cloud Properties from MODIS Observations." Journal of Climate 21, no. 19 (October 1, 2008): 4955–73. http://dx.doi.org/10.1175/2008jcli1974.1.
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Abstract To aid in understanding the role that marine boundary layer (MBL) clouds play in climate and assist in improving their representations in general circulation models (GCMs), their long-term microphysical and macroscale characteristics are quantified using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the National Aeronautics and Space Administration’s (NASA’s) Terra satellite. Six years of MODIS pixel-level cloud products are used from oceanic study regions off the west coasts of California, Peru, the Canary Islands, Angola, and Australia where these cloud types are common. Characterizations are given for their organization (macroscale structure), the associated microphysical properties, and the seasonal dependencies of their variations for scales consistent with the size of a GCM grid box (300 km × 300 km). MBL mesoscale structure is quantified using effective cloud diameter CD, which is introduced here as a simplified measure of bulk cloud organization; it is straightforward to compute and provides descriptive information beyond that offered by cloud fraction. The interrelationships of these characteristics are explored while considering the influences of the MBL state, such as the occurrence of drizzle. Several commonalities emerge for the five study regions. MBL clouds contain the best natural examples of plane-parallel clouds, but overcast clouds occur in only about 25% of the scenes, which emphasizes the importance of representing broken MBL cloud fields in climate models (that are subgrid scale). During the peak months of cloud occurrence, mesoscale organization (larger CD) increases such that the fractions of scenes characterized as “overcast” and “clumped” increase at the expense of the “scattered” scenes. Cloud liquid water path and visible optical depth usually trend strongly with CD, with the largest values occurring for scenes that are drizzling. However, considerable interregional differences exist in these trends, suggesting that different regression functionalities exist for each region. For peak versus off-peak months, the fraction of drizzling scenes (as a function of CD) are similar for California and Angola, which suggests that a single probability distribution function might be used for their drizzle occurrence in climate models. The patterns are strikingly opposite for Peru and Australia; thus, the contrasts among regions may offer a test bed for model simulations of MBL drizzle occurrence.
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Garfinkel, Chaim I., Ori Adam, Efrat Morin, Yehoudah Enzel, Eilat Elbaum, Maya Bartov, Dorita Rostkier-Edelstein, and Uri Dayan. "The Role of Zonally Averaged Climate Change in Contributing to Intermodel Spread in CMIP5 Predicted Local Precipitation Changes." Journal of Climate 33, no. 3 (February 1, 2020): 1141–54. http://dx.doi.org/10.1175/jcli-d-19-0232.1.
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AbstractWhile CMIP5 models robustly project drying of the subtropics and more precipitation in the tropics and subpolar latitudes by the end of the century, the magnitude of these changes in precipitation varies widely across models: for example, some models simulate no drying in the eastern Mediterranean while others simulate more than a 50% reduction in precipitation relative to the model-simulated present-day value. Furthermore, the factors leading to changes in local subtropical precipitation remain unclear. The importance of zonal-mean changes in atmospheric structure for local precipitation changes is explored in 42 CMIP5 models. It is found that up to half of the local intermodel spread over the Mediterranean, northern Mexico, East Asia, southern Africa, southern Australia, and southern South America is related to the intermodel spread in large-scale processes such as the magnitude of globally averaged surface temperature increases, Hadley cell widening, polar amplification, stabilization of the tropical upper troposphere, or changes in the polar stratosphere. Globally averaged surface temperature increases account for intermodel spread in land subtropical drying in the Southern Hemisphere but are not important for land drying adjacent to the Mediterranean. The factors associated with drying over the eastern Mediterranean and western Mediterranean differ, with stabilization of the tropical upper troposphere being a crucial factor for the former only. Differences in precipitation between the western and eastern Mediterranean are also evident on interannual time scales. In contrast, the global factors examined here are unimportant over most of the United States, and more generally over the interior of continents. Much of the rest of the spread can be explained by variations in local relative humidity, a proxy also for zonally asymmetric circulation and thermodynamic changes.
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Sachindra, D. A., F. Huang, A. Barton, and B. J. C. Perera. "Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions." Journal of Water and Climate Change 5, no. 4 (July 15, 2014): 496–525. http://dx.doi.org/10.2166/wcc.2014.056.
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The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor–predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.
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Stober, Gunter, Alan Liu, Alexander Kozlovsky, Zishun Qiao, Ales Kuchar, Christoph Jacobi, Chris Meek, et al. "Meteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithm." Atmospheric Measurement Techniques 15, no. 19 (October 13, 2022): 5769–92. http://dx.doi.org/10.5194/amt-15-5769-2022.
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Abstract. Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution.
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Sachindra, D. A., F. Huang, A. Barton, and B. J. C. Perera. "Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach." Journal of Water and Climate Change 7, no. 4 (March 18, 2016): 683–707. http://dx.doi.org/10.2166/wcc.2016.021.
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Using a key station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950–2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key station. The stations that were highly correlated with that key station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key stations and the member stations for each calendar month. The downscaling models at the key stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000–2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.
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Ryder, J., J. Polcher, P. Peylin, C. Ottlé, Y. Chen, E. van Gorsel, V. Haverd, et al. "A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations." Geoscientific Model Development 9, no. 1 (January 25, 2016): 223–45. http://dx.doi.org/10.5194/gmd-9-223-2016.
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Abstract. In Earth system modelling, a description of the energy budget of the vegetated surface layer is fundamental as it determines the meteorological conditions in the planetary boundary layer and as such contributes to the atmospheric conditions and its circulation. The energy budget in most Earth system models has been based on a big-leaf approach, with averaging schemes that represent in-canopy processes. Furthermore, to be stable, that is to say, over large time steps and without large iterations, a surface layer model should be capable of implicit coupling to the atmospheric model. Surface models with large time steps, however, have difficulties in reproducing consistently the energy balance in field observations. Here we outline a newly developed numerical model for energy budget simulation, as a component of the land surface model ORCHIDEE-CAN (Organising Carbon and Hydrology In Dynamic Ecosystems – CANopy). This new model implements techniques from single-site canopy models in a practical way. It includes representation of in-canopy transport, a multi-layer long-wave radiation budget, height-specific calculation of aerodynamic and stomatal conductance, and interaction with the bare-soil flux within the canopy space. Significantly, it avoids iterations over the height of the canopy and so maintains implicit coupling to the atmospheric model LMDz (Laboratoire de Météorologie Dynamique Zoomed model). As a first test, the model is evaluated against data from both an intensive measurement campaign and longer-term eddy-covariance measurements for the intensively studied Eucalyptus stand at Tumbarumba, Australia. The model performs well in replicating both diurnal and annual cycles of energy and water fluxes, as well as the vertical gradients of temperature and of sensible heat fluxes.
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Ryder, J., J. Polcher, P. Peylin, C. Ottlé, Y. Chen, E. van Gorsel, V. Haverd, et al. "A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations." Geoscientific Model Development Discussions 7, no. 6 (December 8, 2014): 8649–701. http://dx.doi.org/10.5194/gmdd-7-8649-2014.
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Abstract. In Earth system modelling, a description of the energy budget of the vegetated surface layer is fundamental as it determines the meteorological conditions in the planetary boundary layer and as such contributes to the atmospheric conditions and its circulation. The energy budget in most Earth system models has long been based on a "big-leaf approach", with averaging schemes that represent in-canopy processes. Such models have difficulties in reproducing consistently the energy balance in field observations. We here outline a newly developed numerical model for energy budget simulation, as a component of the land surface model ORCHIDEE-CAN (Organising Carbon and Hydrology In Dynamic Ecosystems – CANopy). This new model implements techniques from single-site canopy models in a practical way. It includes representation of in-canopy transport, a multilayer longwave radiation budget, height-specific calculation of aerodynamic and stomatal conductance, and interaction with the bare soil flux within the canopy space. Significantly, it avoids iterations over the height of tha canopy and so maintains implicit coupling to the atmospheric model LMDz. As a first test, the model is evaluated against data from both an intensive measurement campaign and longer term eddy covariance measurements for the intensively studied Eucalyptus stand at Tumbarumba, Australia. The model performs well in replicating both diurnal and annual cycles of fluxes, as well as the gradients of sensible heat fluxes. However, the model overestimates sensible heat flux against an underestimate of the radiation budget. Improved performance is expected through the implementation of a more detailed calculation of stand albedo and a more up-to-date stomatal conductance calculation.
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Majda, Andrew J., Boris Gershgorin, and Yuan Yuan. "Low-Frequency Climate Response and Fluctuation–Dissipation Theorems: Theory and Practice." Journal of the Atmospheric Sciences 67, no. 4 (April 1, 2010): 1186–201. http://dx.doi.org/10.1175/2009jas3264.1.
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Abstract The low-frequency response to changes in external forcing or other parameters for various components of the climate system is a central problem of contemporary climate change science. The fluctuation–dissipation theorem (FDT) is an attractive way to assess climate change by utilizing statistics of the present climate; with systematic approximations, it has been shown recently to have high skill for suitable regimes of an atmospheric general circulation model (GCM). Further applications of FDT to low-frequency climate response require improved approximations for FDT on a reduced subspace of resolved variables. Here, systematic mathematical principles are utilized to develop new FDT approximations on reduced subspaces and to assess the small yet significant departures from Gaussianity in low-frequency variables on the FDT response. Simplified test models mimicking crucial features in GCMs are utilized here to elucidate these issues and various FDT approximations in an unambiguous fashion. Also, the shortcomings of alternative ad hoc procedures for FDT in the recent literature are discussed here. In particular, it is shown that linear regression stochastic models for the FDT response always have no skill for a general nonlinear system for the variance response and can have poor or moderate skill for the mean response depending on the regime of the Lorenz 40-model and the details of the regression strategy. New nonlinear stochastic FDT approximations for a reduced set of variables are introduced here with significant skill in capturing the effect of subtle departures from Gaussianity in the low-frequency response for a reduced set of variables.
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LINK, RAINER, and HORST-JOACHIM LÜDECKE. "A NEW BASIC ONE-DIMENSIONAL ONE-LAYER MODEL OBTAINS EXCELLENT AGREEMENT WITH THE OBSERVED EARTH TEMPERATURE." International Journal of Modern Physics C 22, no. 05 (May 2011): 449–55. http://dx.doi.org/10.1142/s0129183111016361.
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The Earth radiation and energy budget is calculated by a manifold of rather complex Global Circulation Models. Their outcome mostly cannot identify integral radiation or energy budget relations. Therefore it is reasonable to look at more basic models to identify the main aspects of the model results. The simplest one of all of those is a one-dimensional one-layer model. However, most of these models — two are discussed here — suffer the drawback that they do not include essential contributions and relations between the atmospheric layer and the Earth. The one-dimensional one-layer model presented here integrates sensible and latent heat, the absorption of solar radiation and the direct emission of the long wave radiation to space in addition to the standard correlations. For the atmospheric layer two different long wave fluxes are included, top of atmosphere to space and bulk emission to Earth. The reflections of long wave radiation are taken into account. It is shown that this basic model is in excellent agreement with the observed integrated global energy budget.
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Zanna, Laure, and Eli Tziperman. "Optimal Surface Excitation of the Thermohaline Circulation." Journal of Physical Oceanography 38, no. 8 (August 1, 2008): 1820–30. http://dx.doi.org/10.1175/2008jpo3752.1.
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Abstract The amplification of thermohaline circulation (THC) anomalies resulting from heat and freshwater forcing at the ocean surface is investigated in a zonally averaged coupled ocean–atmosphere model. Optimal initial conditions of surface temperature and salinity leading to the largest THC growth are computed, and so are the structures of stochastic surface temperature and salinity forcing that excite maximum THC variance (stochastic optimals). When the THC amplitude is defined as its sum of squares (equivalent to using the standard L2 norm), the nonnormal linearized dynamics lead to an amplification with a time scale on the order of 100 yr. The optimal initial conditions have a vanishing THC anomaly, and the complex amplification mechanism involves the advection of both temperature and salinity anomalies by the mean flow and of the mean temperature and salinity by the anomaly flow. The L2 characterization of THC anomalies leads to physically interesting results, yet to a mathematically singular problem. A novel alternative characterizing the THC amplitude by its maximum value, as often done in general circulation model studies, is therefore introduced. This complementary method is shown to be equivalent to using the L-infinity norm, and the needed mathematical approach is developed and applied to the THC problem. Under this norm, an amplification occurs within 10 yr explained by the classic salinity advective feedback mechanism. The analysis of the stochastic optimals shows that the character of the THC variability may be very sensitive to the spatial pattern of the surface forcing. In particular, a maximum THC variance and long-time-scale variability are excited by a basin-scale surface forcing pattern, while a significantly higher frequency and to some extent a weaker variability are induced by a smooth and large-scale, yet mostly concentrated in polar areas, surface forcing pattern. Overall, the results suggest that a large THC variability can be efficiently excited by atmospheric surface forcing, and the simple model used here makes several predictions that would be interesting to test using more complex models.
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Biello, Joseph A., and Andrew J. Majda. "A New Multiscale Model for the Madden–Julian Oscillation." Journal of the Atmospheric Sciences 62, no. 6 (June 1, 2005): 1694–721. http://dx.doi.org/10.1175/jas3455.1.
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Abstract A multiscale model of the MJO is developed here that accounts, in a simplified fashion, for both the upscale transfer from synoptic to planetary scales of momentum and temperature from wave trains of thermally driven equatorial synoptic-scale circulations in a moving convective envelope as well as direct mean heating on planetary scales. This model involves idealized thermally driven congestus synoptic-scale fluctuations in the eastern part of the moving wave envelope and convective superclusters in the western part of the envelope. The model self-consistently reproduces qualitatively many of the detailed structural features of the planetary circulation in the observations of the MJO, including the vertical structure in both the westerly onset region and the strong westerly wind burst region, as well as the horizontal quadrupole planetary vortex structure. The westerly midlevel inflow in the strong westerly region and the quadrupole vortex are largely produced in the model by the upscale transport of momentum to the planetary scales, while the midlevel easterly jet in the westerly onset region is substantially strengthened by this process. The role of wave trains of tilted organized synoptic-scale circulations is crucial for this fidelity with observations. The appeal of the multiscale models developed below is their firm mathematical underpinnings, simplicity, and analytic tractability while remaining self-consistent with many of the features of the observational record.
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Dadson, Simon. "Geomorphology and Earth system science." Progress in Physical Geography: Earth and Environment 34, no. 3 (June 2010): 385–98. http://dx.doi.org/10.1177/0309133310365031.
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Earth system science (ESS) is an approach to: ‘obtain a scientific understanding of the entire Earth system on a global scale by describing how its component parts and their interactions have evolved, how they function, and how they may be expected to continue to evolve on all timescales’ (Bretherton, 1998). The aim of this review is to introduce some key examples showing the role of Earth surface processes, the traditional subject of geomorphology, within the interacting Earth system. The paper considers three examples of environmental systems in which geomorphology plays a key role: (1) links between topography, tectonics, and atmospheric circulation; (2) links between geomorphic processes and biogeochemical cycles; and (3) links between biological processes and the Earth’s surface. Key research needs are discussed, including the requirement for better opportunities for interdisciplinary collaboration, clearer mathematical frameworks for Earth system models, and more sophisticated interaction between natural and social scientists.
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Oliver, Eric C. J., and Neil J. Holbrook. "A Statistical Method for Improving Continental Shelf and Nearshore Marine Climate Predictions." Journal of Atmospheric and Oceanic Technology 31, no. 1 (January 1, 2014): 216–32. http://dx.doi.org/10.1175/jtech-d-13-00052.1.
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Abstract Spatially and temporally homogeneous measurements of ocean temperature variability at high resolution on the continental shelf are scarce. Daily estimates of large-scale ocean properties are readily available from global ocean reanalysis products. However, the ocean models that underpin these reanalysis products tend not to have been designed for the simulation of complex coastal ocean variability. Hence, across-shelf values are often poorly represented. This study involved developing a statistical approach to more accurately and robustly represent SST on the continental shelf informed by large-scale satellite observations and reanalysis data or model output. Using the southeastern Australian shelf region as a case study, this paper demonstrates that this statistical model approach generates more accurate estimates of the inshore SST using (i) offshore SST from Bluelink Reanalysis (BRAN) and (ii) the statistical relationship between inshore and offshore SST in observations from the Advanced Very High Resolution Radiometer. SST is separated into the mean, seasonal cycle, and residual variability, and separate models are developed for each component. The offshore locations used to inform the model are determined by taking into account (i) the quality of BRAN at each location, (ii) the strength between the inshore and offshore variability, and (iii) the proximity of the inshore and offshore locations. Model predictions are made for the continental shelf around southeastern Australia. The role of the mean circulation in providing connectivity between the shelf and the offshore regions is discussed, and how this information can be used to better inform the choice of model predictor locations, leading to a hybrid statistical–connectivity model.
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Ma, Hsi-Yen, C. Roberto Mechoso, Yongkang Xue, Heng Xiao, J. David Neelin, and Xuan Ji. "On the Connection between Continental-Scale Land Surface Processes and the Tropical Climate in a Coupled Ocean–Atmosphere–Land System." Journal of Climate 26, no. 22 (October 29, 2013): 9006–25. http://dx.doi.org/10.1175/jcli-d-12-00819.1.
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Abstract An evaluation is presented of the impact on tropical climate of continental-scale perturbations given by different representations of land surface processes (LSPs) in a general circulation model that includes atmosphere–ocean interactions. One representation is a simple land scheme, which specifies climatological albedos and soil moisture availability. The other representation is the more comprehensive Simplified Simple Biosphere Model, which allows for interactive soil moisture and vegetation biophysical processes. The results demonstrate that such perturbations have strong impacts on the seasonal mean states and seasonal cycles of global precipitation, clouds, and surface air temperature. The impact is especially significant over the tropical Pacific Ocean. To explore the mechanisms for such impact, model experiments are performed with different LSP representations confined to selected continental-scale regions where strong interactions of climate–vegetation biophysical processes are present. The largest impact found over the tropical Pacific is mainly from perturbations in the tropical African continent where convective heating anomalies associated with perturbed surface heat fluxes trigger global teleconnections through equatorial wave dynamics. In the equatorial Pacific, the remote impacts of the convection anomalies are further enhanced by strong air–sea coupling between surface wind stress and upwelling, as well as by the effects of ocean memory. LSP perturbations over South America and Asia–Australia have much weaker global impacts. The results further suggest that correct representations of LSP, land use change, and associated changes in the deep convection over tropical Africa are crucial to reducing the uncertainty of future climate projections with global climate models under various climate change scenarios.
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Kumar, Vickal V., Alain Protat, Christian Jakob, Christopher R. Williams, Surendra Rauniyar, Graeme L. Stephens, and Peter T. May. "The Estimation of Convective Mass Flux from Radar Reflectivities." Journal of Applied Meteorology and Climatology 55, no. 5 (May 2016): 1239–57. http://dx.doi.org/10.1175/jamc-d-15-0193.1.
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AbstractCumulus parameterizations in general circulation models (GCMs) frequently apply mass-flux schemes in their description of tropical convection. Mass flux constitutes the product of the fractional area covered by cumulus clouds in a model grid box and the vertical velocity within the cumulus clouds. The cumulus area fraction profiles can be derived from precipitating radar reflectivity volumes. However, the vertical velocities are difficult to observe, making the evaluation of mass-flux schemes difficult. In this paper, the authors develop and evaluate a parameterization of vertical velocity in convective (cumulus) clouds using only radar reflectivities collected by a C-band polarimetric research radar (CPOL), operating at Darwin, Australia. The parameterization is trained using vertical velocity retrievals from a dual-frequency wind profiler pair located within the field of view of CPOL. The parametric model uses two inputs derived from CPOL reflectivities: the 0-dBZ echo-top height (0-dBZ ETH) and a height-weighted column reflectivity index (ZHWT). The 0-dBZ ETH determines the shape of the vertical velocity profile, while ZHWT determines its strength. The evaluation of these parameterized vertical velocities using (i) the training dataset, (ii) an independent wind-profiler-based dataset, and (iii) 1 month of dual-Doppler vertical velocity retrievals indicates that the statistical representation of vertical velocity is reasonably accurate up to the 75th percentile. However, the parametric model underestimates the extreme velocities. The method allows for the derivation of cumulus mass flux and its variability on current GCM scales based only on reflectivities from precipitating radar, which could be valuable to modelers.
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Zeng, Xiping, Wei-Kuo Tao, Scott W. Powell, Robert A. Houze, Paul Ciesielski, Nick Guy, Harold Pierce, and Toshihisa Matsui. "A Comparison of the Water Budgets between Clouds from AMMA and TWP-ICE." Journal of the Atmospheric Sciences 70, no. 2 (February 1, 2013): 487–503. http://dx.doi.org/10.1175/jas-d-12-050.1.
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Abstract Two field campaigns, the African Monsoon Multidisciplinary Analysis (AMMA) and the Tropical Warm Pool–International Cloud Experiment (TWP-ICE), took place in 2006 near Niamey, Niger, and Darwin, Northern Territory, Australia, providing extensive observations of mesoscale convective systems (MCSs) near a desert and a tropical coast, respectively. Under the constraint of their observations, three-dimensional cloud-resolving model simulations are carried out and presented in this paper to replicate the basic characteristics of the observed MCSs. All of the modeled MCSs exhibit a distinct structure having deep convective clouds accompanied by stratiform and anvil clouds. In contrast to the approximately 100-km-scale MCSs observed in TWP-ICE, the MCSs in AMMA have been successfully simulated with a scale of about 400 km. These modeled AMMA and TWP-ICE MCSs offer an opportunity to understand the structure and mechanism of MCSs. Comparing the water budgets between AMMA and TWP-ICE MCSs suggests that TWP-ICE convective clouds have stronger ascent while the mesoscale ascent outside convective clouds in AMMA is stronger. A case comparison, with the aid of sensitivity experiments, also suggests that vertical wind shear and ice crystal (or dust aerosol) concentration can significantly impact stratiform and anvil clouds (e.g., their areas) in MCSs. In addition, the obtained water budgets quantitatively describe the transport of water between convective, stratiform, and anvil regions as well as water sources/sinks from microphysical processes, providing information that can be used to help determine parameters in the convective and cloud parameterizations in general circulation models (GCMs).
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Brown, Josephine R., Christian Jakob, and John M. Haynes. "An Evaluation of Rainfall Frequency and Intensity over the Australian Region in a Global Climate Model." Journal of Climate 23, no. 24 (December 15, 2010): 6504–25. http://dx.doi.org/10.1175/2010jcli3571.1.
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Abstract Observed regional rainfall characteristics can be analyzed by examining both the frequency and intensity of different categories of rainfall. A complementary approach is to consider rainfall characteristics associated with regional synoptic regimes. These two approaches are combined here to examine daily rainfall characteristics over the Australian region, providing a target for model simulations. Using gridded daily rainfall data for the period 1997–2007, rainfall at each grid point and averaged over several sites is decomposed into the frequency of rainfall events and the intensity of rainfall associated with each event. Daily sea level pressure is classified using a self-organizing map, and rainfall on corresponding days is assigned to the resulting synoptic regimes. This technique is then used to evaluate rainfall in the new Australian Community Climate and Earth-System Simulator (ACCESS) global climate model and separate the influence of large-scale circulation errors and errors due to the representation of subgrid-scale physical processes. The model exhibits similar biases to many other global climate models, simulating too frequent light rainfall and heavy rainfall of insufficient intensity. These errors are associated with particular synoptic regimes over different sectors of the Australian continent and surrounding oceans. The model simulates only weak convective rainfall over land during the summer monsoon, and heavy rainfall associated with frontal systems over southern Australia is also not simulated. As the model captures the structure and frequency of synoptic patterns, but not the associated rainfall intensity or frequency, it is likely that the source of the rainfall errors lies in model physical parameterizations rather than large-scale dynamics.
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Jackson, Robert, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell. "The development of rainfall retrievals from radar at Darwin." Atmospheric Measurement Techniques 14, no. 1 (January 4, 2021): 53–69. http://dx.doi.org/10.5194/amt-14-53-2021.
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Abstract. The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 1<R<10mmh-1. These rates primarily originate from stratiform clouds and weak convection (median drop diameters less than 1.5 mm). The dual-polarization specific differential phase and differential reflectivity increase in usefulness for rainfall estimators in times with R>10mmh-1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1<R<10mmh-1 (stratiform rain) and when R>10mmh-1 (deeper convective rain).