Dr. Charles Jackson
Publications
Regional response and uncertainties of climate predictions
Wan, X., P. Chang, C. S. Jackson, L. Ji, and M. Li, Plausible effect of climate model bias on abrupt climate change simulations in Atlantic sector, Deep-Sea Res., 58, 1904-1913, 2011, 1 citation, doi:10.1016/j.dsr2.2010.10.068, #2354 
Although considerable progress towards reducing tropical climate biases in the tropical Pacific has been made in many current-generation of climate models over the past decades, reducing large biases and maintaining good agreement with the observations in the tropical Atlantic is still a major challenge and this deficiency may seriously degrade the credibility of the models in their simulation and projection of future climate change in the Atlantic sector. In this paper, we show that the bias in the eastern equatorial Atlantic has a major effect on sea-surface temperature (SST) response to a rapid change in the Atlantic Meridional Overturning Circulation (AMOC). By comparing identical water hosing experiments conducted with two different coupled general circulation models, we dissect oceanic mechanisms underlying the difference in models' SST response. The results show that the different SST response is plausibly attributed to systematic differences in the simulated tropical Atlantic ocean circulation. Therefore, in order to accurately simulate past abrupt climate changes and project future changes, the bias in climate models must be reduced.
Yokohata, T., J. D. Annan, M. Collins, C. S. Jackson, M. Tobis, M. Webb, D. Sexton, and J. C. Hargreaves, Reliability of multi-model and structurally different single-model ensembles, Climate Dynamics, (in press), 2011, doi:10.1007/s00382-011-1203-1, #2422
The performance of several state-of-the-art climate model ensembles, including two multi-model ensembles (MMEs) and four structurally different (perturbed parameter) single model ensembles (SMEs), are investigated for the first time using the rank histogram approach. In this method, the reliability of a model ensemble is evaluated from the point of view of whether the observations can be regarded as being sampled from the ensemble. Our analysis reveals that, in the MMEs, the climate variables we investigated are broadly reliable on the global scale, with a tendency towards overdispersion. On the other hand, in the SMEs, the reliability differs depending on the ensemble and variable field considered. In general, the mean state and historical trend of surface air temperature, and mean state of precipitation are reliable in the SMEs. However, variables such as sea level pressure or top-of-atmosphere clear-sky shortwave radiation do not cover a sufficiently wide range in some. It is not possible to assess whether this is a fundamental feature of SMEs generated with particular model, or a consequence of the algorithm used to select and perturb the values of the parameters. As under-dispersion is a potentially more serious issue when using ensembles to make projections, we recommend the application of rank histograms to assess reliability when designing and running perturbed physics SMEs.
Banner, J. L., C. S. Jackson, L. Yang, K. Hayhoe, C. Woodhouse, L. E. Gulden, K. Jacobs, G. North, R. Leung, W. Washington, X. Jiang, and R. Casteel, Climate change impacts on Texas water: A white paper assessment of the past, present and future and recommendations for action, Texas Water J., 1, 1-19, 2010, #2307
Texas comprises the eastern portion of the Southwest region, where the convergence of climatological and geopolitical
Jackson, C. S., M. K. Sen, P. L. Stoffa, and G. Huerta, Data directed importance sampling for climate model uncertainty estimation, in Advanced Computational Infrastructures for Parallel/Distributed Adaptive Applications, edited by M. Parashar, X. Li, and S. Chandra, editors, Wiley Publishers, 2010, #1912
Jackson, C. S., O. Marchal, Y. Liu, S. Lu, and G. Thompson W, A box model test of the freshwater forcing hypothesis of abrupt climate change and the physics governing ocean stability, Paleoceanography, 25, PA4222, 2010, doi:10.1029/2010PA001936, #2306
Observations and an ocean box model are combined in order to test the adequacy of the freshwater forcing hypothesis to explain abrupt climate change given the uncertainties in the parameterization of vertical buoyancy transport in the ocean. The combination is carried out using Bayesian stochastic inversion, which allows us to infer changes in the mass balance of Northern Hemisphere (NH) ice sheets and in the meridional transports of mass and heat in the Atlantic Ocean that would be required to explain Dansgaardâââ¬ÃÂOeschger Interstadials (DOIs) from 30 to 39 kyr B.P. The mean sea level changes implied by changes in NH ice sheet mass balance agree in amplitude and timing with reconstructions from the geologic record, which gives some support to the freshwater forcing hypothesis. The inversion suggests that the duration of the DOIs should be directly related to the growth of land ice. Our results are unaffected by uncertainties in the representation of vertical buoyancy transport in the ocean. However, the solutions are sensitive to assumptions about physical processes at polar latitudes.
Jackson, C. S., Use of Bayesian inference and data to improve simulations of multi-physics climate phenomena, J. Physics: Conference Series, 180, 01209, 2009, doi:10.1088/1742-6596/180/1/012029, #2130
Bayesian inference provides a means incorporate activities usually associated with post-model development evaluation into the process of model development. Presented is a review of the factors that make this approach challenging, strategies for making the process practical for model development of complex multi-physics phenomena, and suggestions on areas requiring additional research. An analysis is presented of the strategy that was used to determine the joint probability for six non-linearly related parameters important to clouds and convection within the NCAR Community Atmosphere Model version 3.1.
Jackson, C. S., M. K. Sen, G. Huerta, Y. Deng, and K. P. Bowman, Error reduction and convergence in climate prediction, J. Climate, 21, 6698-6709, 2008, 8 citations, doi:10.1175/2008JCLI2112.1, #2029
Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO2 concentrations. Stochastic optimization is used to mimic how six independent climate model development efforts might use the same atmospheric general circulation model, set of observational constraints, and model skill criteria to choose different settings for parameters thought to be important sources of uncertainty related to clouds and convection. Each optimized model improved its skill with respect to observations selected as targets of model development. Of particular note were the improvements seen in reproducing observed extreme rainfall rates over the tropical Pacific, which was not specifically targeted during the optimization process. As compared to the default model sensitivity of 2.4°C, the ensemble of optimized model configurations had a larger and narrower range of sensitivities around 3°C but with different regional responses related to the uncertain choice in optimized parameter settings. These results suggest current generation models, if similarly optimized, may become more convergent in their measure of global sensitivity to greenhouse gas forcing. However, this exploration of the possible sources of modeling and observational uncertainty is not exhaustive. The optimization process illustrates an objective means for selecting an ensemble of plausible climate model configurations that quantify a portion of the uncertainty in the climate model development process.
Villagran, A., G. Huerta, C. S. Jackson, and M. K. Sen, Computational methods for parameter estimation in climate models, Bayesian Analysis, 3, 823-850, 2008, 3 citations, doi:10.1214/08-BA331, #2101
Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncer- tainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling effi- ciency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. The performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embed- ded in these Monte Carlo algorithms. We show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However the adaptive methods can also be limited by inad- equate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the cli- mate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems out- side climate prediction, particularly those where sampling is limited by availability of computational resources.
Deng, Y., K. P. Bowman, and C. S. Jackson, Differences in rain rate intensities between TRMM observations and the community atmosphere model simulations, Geophys. Res. Lett., 34, L01808, 2007, 2 citations, doi:10.1029/2006GL027246, #1849
Precipitation related latent heating is important in driving the atmospheric general circulation and in generating intraseasonal to decadal atmospheric variability. Our ability to project future climate change, especially trends in costly precipitation extremes, hinges upon whether coupled GCMs capture processes that affect precipitation characteristics. Our study compares the tropical-subtropical precipitation characteristics of simulations by the NCAR CAM3.1 atmospheric GCM and observations derived from the NASA Tropical Rainfall Measuring Mission (TRMM) satellite. Despite a fairly good simulation of the annual mean rain rate, CAM rains about 10â50% more often than the real world and fails to capture heavy rainfall associated with deep convective systems over subtropical South America and U.S. Southern Plains. When it rains, there is a likelihood of 0.96â1.0 that it rains lightly in the model, compared to values of 0.84â1.0 in TRMM data. On the other hand, the likelihood of the occurrence of moderate to heavy rainfall is an order of magnitude higher in observations (0.12â0.2) than that in the model (<0.02). Comparison of regionally aggregated PDFs of the rain rate shows that CAM underestimates the probability of NOT raining, overestimates the probability of light rain and almost completely misses the tails of the PDFs. The model compensates for the lack of heavy precipitation through raining more frequently within the light rain category, which leads to an annual rainfall amount close to what is observed. CAM captures the qualitative change of rain rate PDF from a âdryâ oceanic to a âwetâ oceanic region, but it fails to simulate the change of precipitation characteristics from an oceanic region to a land region where thunderstorm rainfall dominates.
Gulden, L. E., E. Rosero, G. Kocurek [], M. Rodell, C. S. Jackson, G.-Y. Niu, P. J.-F. Yeh, and J. Famiglietti, Improving land-surface model hydrology: Is an explicit aquifer model better than a deeper soil profile?, Geophys. Res. Lett., 34, L09402, 2007, 22 citations, doi:10.1029/2007GL029804, #1918
We use Monte Carlo analysis to show that explicit representation of an aquifer within a land-surface model (LSM) decreases the dependence of model performance on accurate selection of subsurface hydrologic parameters. Within the National Center for Atmospheric Research Community Land Model (CLM) we evaluate three parameterizations of vertical water flow: (1) a shallow soil profile that is characteristic of standard LSMs; (2) an extended soil profile that allows for greater variation in terrestrial water storage; and (3) a lumped, unconfined aquifer model coupled to the shallow soil profile. North American Land Data Assimilation System meteorological forcing data (1997â2005) drive the models as a single column representing Illinois, USA. The three versions of CLM are each run 22,500 times using a random sample of the parameter space for soil texture and key hydrologic parameters. Other parameters remain constant. Observation-based monthly changes in state-averaged terrestrial water storage (dTWS) are used to evaluate the model simulations. After single-criteria parameter exploration, the schemes are equivalently adept at simulating dTWS. However, explicit representation of groundwater considerably decreases the sensitivity of modeled dTWS to errant parameter choices. We show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a congruent set.
Marchal, O., C. S. Jackson, J. Nilsson, A. Paul, and T. F. Stocker, Buoyancy-driven flow and nature of vertical mixing in a zonally averaged model, in Ocean Circulation: Mechanisms and Impacts, edited by A. Schmittner, J. C. H. Chiang, and S. R. Hemming, American Geophysical Union, Washington, DC, AGU Monograph, 173, 33-52, 2007, doi:10.1029/173GM05, #1890
The consequences for the meridional overturning circulation (MOC) of fundamentally different assumptions about the vertical effective diffusivity of heat and salt (κv) are examined in a zonally averaged model of the buoyancy-driven flow in one- and two-hemisphere basins. First, we replicate results obtained in earlier studies from a zonally averaged model based on a less elaborate closure for the zonal pressure difference. For a single-hemisphere basin, the equilibrium response of the MOC to freshwater forcing (salt addition at low latitudes and salt extraction at high latitudes) depends qualitatively on the nature of vertical mixing: if the diffusivity is constant (a common assumption), the MOC decreases with increased forcing, whereas if it depends on vertical density stratification (at least an equally plausible assumption) the MOC increases with increased forcing. For a two-hemisphere basin, on the other hand, the equilibrium response of the MOC in the dominant
hemisphere to increased freshwater forcing (symmetric about the equator) is an amplification for both mixing representations. Second, we investigate the instability of the flow at large freshwater forcing. For both basins, the flow is more stable
to the forcing if κv varies with vertical stratification. For a single-hemisphere basin, self-sustained oscillations of the flow that are quasi-periodic (e.g., millennial) are found for both fixed and stability-dependent κv. For a two-hemisphere basin, such oscillations are found only when κv is stability-dependent. For both basins, the occurrence and period of the oscillations are partly determined by the energy available for vertical mixing if κv varies with vertical stratification. A possible analogy with the relaxation oscillations of van der Pol is presented.
Jackson, C. S., Y. Liu, and O. Marchal, Can models of abrupt climate change be tested from sea level reconstructions?, Pages News, 14 (2), 24-26, 2006, #1850
Jackson, C. S., Quaternary environments, in Encyclopedia of the Quaternary Science, edited by S. A. Elias, Elsevier, 2006, #1851
Hall, A., A. Clement, D. Thompson W, A. J. Broccoli, and C. S. Jackson, The importance of atmospheric dynamics in the northern hemisphere wintertime climate response to changes in the Earth's orbit, J. Climate, 18, 1315-1325, 2005, 10 citations, #1733
Milankovitch proposed that variations in the earthâs orbit cause climate variability through a local thermodynamic response to changes in insolation. This hypothesis is tested by examining variability in an atmospheric general circulation model coupled to an ocean mixed layer model subjected to the orbital forcing of the past 165 000 yr. During Northern Hemisphere summer, the modelâs response conforms to Milankovitchâs hypothesis, with high (low) insolation generating warm (cold) temperatures throughout the hemisphere. However, during Northern Hemisphere winter, the climate variations stemming from orbital forcing cannot be solely understood as a local thermodynamic response to radiation anomalies. Instead, orbital forcing perturbs the atmospheric circulation in a pattern bearing a striking resemblance to the northern annular mode, the primary mode of simulated and observed unforced atmospheric variability. The hypothesized reason for this similarity is that the circulation response to orbital forcing reflects the same dynamics generating unforced variability. These circulation anomalies are in turn responsible for significant fluctuations in other climate variables: Most of the simulated orbital signatures in wintertime surface air temperature over midlatitude continents are directly traceable not to local radiative forcing, but to orbital excitation of the northern annular mode. This has paleoclimate implications: during the point of the model integration corresponding to the last interglacial (Eemian) period, the orbital excitation of this mode generates a 1°â2°C warm surface air temperature anomaly over Europe, providing an explanation for the warm anomaly of comparable magnitude implied by the paleoclimate proxy record. The results imply that interpretations of the paleoclimate record must account for changes in surface temperature driven not only by changes in insolation, but also by perturbations in atmospheric dynamics.
Jackson, C. S., M. K. Sen, and P. L. Stoffa, An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model predictions, J. Climate, 17, 2828-2841, 2004, 21 citations, #1626
One source of uncertainty for climate model predictions arises from the fact that climate models have been optimized to reproduce observational means. To quantify the uncertainty resulting from a realistic range of model configurations, it is necessary to estimate a multidimensional probability distribution that quantifies how likely different model parameter combinations are, given knowledge of the uncertainties in the observations. The computational cost of mapping a multidimensional probability distribution for a climate model using traditional means (e.g., Monte Carlo or Metropolis/Gibbs sampling) is impractical, requiring 104â106 model evaluations for problems involving less than 10 parameters. This paper examines whether such a calculation is more feasible using a particularly efficient but approximate algorithm called Bayesian stochastic inversion, based on multiple very fast simulated annealing (VFSA). Investigated here is how the number of model parameters, natural variability, and the degree of nonlinearity affect the computational cost and accuracy of estimating parameter uncertainties within a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric GCM. In general, multiple VFSA is one to two orders of magnitude more efficient than the Metropolis/Gibbs sampler, depending primarily on dimensionality of the parameter space analysis. The average cost of estimating parameter uncertainties is only moderately affected by noise within the model as long as the signal-to-noise ratio is greater than 5. Also the average cost of estimating parameter uncertainties nearly doubles for problems in which parameters are nonlinearly related.
Mu, Q., C. S. Jackson, and P. L. Stoffa, A multivariate empirical-orthogonal-function-based measure of climate model performance, J. Geophys. Res., 109, D15101, 2004, 3 citations, doi:10.1029/2004JD004584, #1734
A measure of the average distance between climate model predictions of multiple fields and observations has been developed that is based on the use of empirical orthogonal functions (EOFs). The application of EOFs provides a means to use information about spatial correlations in natural variability to provide a more balanced view of the significance of changes in model predictions across multiple fields, seasons, and regions. A comparison is made between the EOF-based measure and measures that are normalized by grid point variance and spatial variance for changes in the National Center for Atmospheric Research Community Climate Model, Version 3.10 (CCM3.10), parameter controlling initial cloud downdraft mass flux (ALFA), an important parameter within the Zhang and McFarlane [1995] convection scheme. All measures present consistent views that increasing ALFA from its default value creates significant improvements in precipitation, shortwave radiation reaching the surface, and surface latent heat fluxes at the expense of degrading predictions of total cloud cover, near-surface air temperature, net shortwave radiation at the top of the atmosphere, and relative humidity. However, the relative importance of each of these changes, and therefore the average view of the change in model performance, is significantly impacted by the details of how each measure of model performance handles regions with little or no internal variability. In general, the EOF-based measure emphasizes regions where modeled-observational differences are large, excluding those regions where internal variability is small.
Xia, Y. L., P. L. Stoffa, C. S. Jackson, and M. K. Sen, Effect of forcing data errors on calibration and uncertainty estimates of the CHASM model: A multi-dataset study, in Observations, Theory, and Modeling of Atmospheric and Oceanic Variability, World Sci. Ser. Meteor. Asia, Singapore, 3, 340-355, 2004, #1669
Xia, Y. L., G. Kocurek [], C. S. Jackson, P. L. Stoffa, and M. K. Sen, Impacts of data length on optimal parameter and uncertainty estimation of a land surface model, J. Geophys. Res., 109, D07101, 2004, 12 citations, doi:10.1029/2003JD004419, #1719
The optimal parameters and uncertainty estimation of land surface models require that appropriate length of forcing and calibration data be selected for computing error functions. Most of the previous studies used less than two years of data to optimize land surface models. In this study, 18-year hydrometeorological data at Valdai, Russia, were used to run the Chameleon Surface Model (CHASM). The optimal parameters were obtained by employing a global optimization technique called very fast simulated annealing. The uncertainties of model parameters were estimated by the Bayesian stochastic inversion technique. Forty-four experiments were conducted by using different lengths of data from the 18-year record, and a total of about 3 million parameter sets were produced. This study found that different calibration variables require different lengths of data to obtain optimal parameters and uncertainty estimates which are insensitive to the period selected. In the case of optimal parameters, monthly root-zone soil moisture, runoff, and evapotranspiration require 8, 3, and 1 years of data, respectively. In the case of uncertainty estimates, monthly root-zone soil moisture, runoff, and evapotranspiration require 8, 8, and 3 years of data, respectively. Spin-up has little impact on the selection of optimal parameters and uncertainty estimates when evapotranspiration and runoff were calibrated. However, spin-up affects the selection of optimal parameters when soil moisture was calibrated.
Xia, Y. L., M. K. Sen, C. S. Jackson, and P. L. Stoffa, Multidataset study of optimal parameter and uncertainty estimation of a land surface model with Bayesian stochastic inversion and multicriteria method, J. Applied Meteor., 43, 1477-1497, 2004, 4 citations, #1720
This study evaluates the ability of Bayesian stochastic inversion (BSI) and multicriteria (MC) methods to search for the optimal parameter sets of the Chameleon Surface Model (CHASM) using prescribed forcing to simulate observed sensible and latent heat fluxes from seven measurement sites representative of six biomes including temperate coniferous forests, tropical forests, temperate and tropical grasslands, temperate crops, and semiarid grasslands. Calibration results with the BSI and MC show that estimated optimal values are very similar for the important parameters that are specific to the CHASM model. The model simulations based on estimated optimal parameter sets perform much better than the default parameter sets. Cross-validations for two tropical forest sites show that the calibrated parameters for one site can be transferred to another site within the same biome. The uncertainties of optimal parameters are obtained through BSI, which estimates a multidimensional posterior probability density function (PPD). Marginal PPD analyses show that nonoptimal choices of stomatal resistance would contribute most to model simulation errors at all sites, followed by ground and vegetation roughness length at six of seven sites. The impact of initial root-zone soil moisture and nonmosaic approach on estimation of optimal parameters and their uncertainties is discussed.
Jackson, C. S., and A. J. Broccoli, Orbital forcing of Arctic climate: Mechanisms of climate response and implications for continental glaciation, Climate Dynamics, 21, 539-557, 2003, 37 citations, doi:10.1007/s00382-003-0351-3, #1615
Progress in understanding how terrestrial ice volume is linked to Earths orbital configuration has been impeded by the cost of simulating climate system processes relevant to glaciation over orbital time scales (103â105 years). A compromise is usually made to represent the climate system by models that are averaged over one or more spatial dimensions or by three-dimensional models that are limited to simulating particular snapshots in time. We take advantage of the short equilibration time (10 years) of a climate model consisting of a three-dimensional atmosphere coupled to a simple slab ocean to derive the equilibrium climate response to accelerated variations in Earths orbital configuration over the past 165,000 years. Prominent decreases in ice melt and increases in snowfall are simulated during three time intervals near 26, 73, and 117 thousand years ago (ka) when aphelion was in late spring and obliquity was low. There were also significant decreases in ice melt and increases in snowfall near 97 and 142 ka when eccentricity was relatively large, aphelion was in late spring, and obliquity was high or near its long term mean. These glaciation-friendly time intervals correspond to prominent and secondary phases of terrestrial ice growth seen within the marine 18O record. Both dynamical and thermal effects contribute to the increases in snowfall during these periods, through increases in storm activity and the fraction of precipitation falling as snow. The majority of the mid- to high latitude response to orbital forcing is organized by the properties of sea ice, through its influence on radiative feedbacks that nearly double the size of the orbital forcing as well as its influence on the seasonal evolution of the latitudinal temperature gradient.
Jackson, C. S., Y. L. Xia, M. K. Sen, and P. L. Stoffa, Optimal parameter and uncertainty estimation of a land surface model: A case example using data from Cabauw, Netherlands, J. Geophys. Res., 108, 4853, 2003, 18 citations, doi:10.1029/2002JD002991, #1627
Land surface models involve a large number of interdependent parameters that affect the physics of how surface energy fluxes are partitioned between latent heat, sensible heat, net radiative, and ground heat fluxes. The goal of an optimal parameter and uncertainty analysis of a land surface model is to identify a range of parameter sets that enable model predictions to be bounded within observational uncertainties. Here we apply Bayesian stochastic inversion (BSI) using very fast simulated annealing (VFSA) to identify parameter sets of the Chameleon surface model (CHASM) land surface model that are consistent with the uncertainty limits ascribed to a high-quality data set collected from Cabauw, Netherlands. These results are compared to the parameter sets obtained through the multicriteria (MC) approach. All analyses evaluate model performance against daily and monthly mean observations of sensible, latent, and ground heat fluxes. BSI and MC identify similar âbest fitâ model parameter sets that improve CHASM performance over default parameter settings. The three most important CHASM parameters at Cabauw are minimum stomatal resistance, vegetation roughness length, and vegetation fraction cover. BSI is based on a Bayesian inference model such that that it expresses uncertainty in terms of a posterior probability density function, different moments of which provide information about parameter means and covariances. Although MC gives a range of possible optimal parameters through the concept of a Pareto set, we found that these ranges did not provide a consistent or representative view of the uncertainty within the observational data. The BSI algorithm in the current study is particularly efficient in that it only requires about double the number of model evaluations than the MC algorithm. This is a substantial saving over other more accurate methods to evaluate uncertainty such as the Metropolis/Gibbs' sampler that requires at least 40 times more computations than the BSI algorithm to obtain similar results.
Jackson, C. S., Sensitivity of stationary wave amplitude to regional changes in Laurentide ice sheet topography in single-layer models of the atmosphere, J. Geophys. Res., 105, 24443-24454, 2000, 6 citations, #1568
Climate variability on millennial timescales has been observed in many geologic records covering the last glacial cycle. A potential source of this variability is the Laurentide ice sheet (LIS) in its periodic discharge of large quantities of icebergs to the North Atlantic. The present analysis considers whether regional variations in LIS topography could exert a significant influence on the atmosphere's stationary wave circulation. The maximum effect that regional changes in LIS topography have on the atmosphere's stationary wave circulation is determined using single-layer models of the atmosphere. Model experiments measure the individual contribution of 4.5° à 7.5° sections of the LIS and Greenland topography to global mean stationary wave amplitude. Results show the possibility for a limited region of topography to control a disproportionate amount of the atmosphere's total response to topography. Moreover, the possibility exists for a reduction in topographic forcing to increase stationary wave amplitude. These results can be understood by considering how the mean flow controls the horizontal propagation of wave energy and superposition of wave amplitude. The location of regions with enhanced stationary wave sensitivity to topographic alteration is found to be sensitive to mean topographic height but not mean wind strength. The latter is found to primarily affect the overall amplitude of sensitivity rather than the pattern. The impact of two hypothetical changes in LIS topography is considered, and they are found to have widely different effects on the global stationary wave field. Stationary wave sensitivity to topography within the single-layer models suggests that variations in the size or shape of the LIS can be one factor important to climate variability on millennial timescales.
More publications
Jackson, C., and A.J. Broccoli (2000) The influence of orbital configuration on Arctic glaciation, Proceedings of the third Paleoclimate Modelling Intercomparison Project (PMIP) Workshop, WCRP-111, 177-178.
Jackson, C. (1999) Sensitivity of stationary wave amplitude to regional changes in Laurentide ice sheet topography, Mechanisms of Global Climate Change at Millennial Time Scales, AGU Geophysical Monograph Vol. 112, Eds. P. Clark, R. Webb and L. Keigwin, 329-337.
Jackson, C. (1997) Sensitivity of a two-layer model atmosphere to changes in ice-sheet topography, Annals of Glaciology, 25, 246-249.
Roelofs, L.D, and C. Jackson (1993) Critical and finite-size behavior of the Heisenberg model with face-centered-cubic anisotropy. Phys. Rev. B 47, 197-201. [pdf]