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Uncertainty in the sensitivity of global mean air temperature to changes in atmospheric CO2 concentration arises from factors controlling the response of cloud amounts. Here, we present an analysis...

We find that part of the uncertainty in the amplitude and pattern of the modeled precipitation response to CO2 forcing traces to tropical condensation not directly involved with parameterized convection. The fraction of tropical rainfall associated with large-scale condensation can vary from a few percent to well over half depending on model details and parameter settings. In turn, because of the coupling between condensation and tropical circulation, the different ways model assumptions affect the large-scale rainfall fraction also affect the patterns of the response within individual models. In two single-model ensembles based on the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM), versions 3.1 and 5.3, we find strong correlations between the fraction of tropical large-scale rain and both climatological rainfall and circulation and the response to CO2 forcing. While the effects of an increasing tropical large-scale rain fraction are opposite in some ways in the two ensembles—for example, the Hadley circulation weakens with the large-scale rainfall fraction in the CAM3.1 ensemble while strengthening in the CAM5.3 ensemble—we can nonetheless understand these different effects in terms of the relationship between latent heating and circulation, and we propose explanations for each ensemble. We compare these results with data from phase 5 of the Coupled Model Intercomparison Project (CMIP5), for which some of the same patterns hold. Given the importance of this partitioning, there is a need for constraining this source of uncertainty using observations. However, since a “large-scale rainfall fraction” is a modeling construct, it is not clear how observations may be used to test various modeling assumptions determining this fraction.

A calibrated single-model ensemble (SME) derived from the NCAR Community Atmosphere Model, version 3.1, is used to test two hypothesized emergent constraints on cloud feedback and equilibrium climate sensitivity (ECS). The Fasullo and Trenberth relative humidity (RH) metric and the Sherwood et al. lower-tropospheric mixing (LTMI) metric are computed for the present-day climate of the SME, and the relationships between the metrics, ECS, and cloud and other climate feedbacks are examined. The tropical convergence zone relative humidity (RHM) and the parameterized lower-tropospheric mixing (LTMIS) are positively correlated to ECS, and each is associated with a different spatial pattern of tropical shortwave cloud feedback in the SME. However, neither of those metrics is linked to the type of cloud response hypothesized by its authors. The resolved lower-tropospheric mixing (LTMID) is positively correlated to ECS for a subset of the SME having LTMID over a threshold value. LTMI and the RH for the dry, descending branch of the Hadley cell (RHD) narrow and shift upward the posterior estimates of ECS in the SME, but the SME bias in RHD and concerns over poorly understood physical mechanisms suggest the narrowing could be spurious for both constraints. While calibrated SME results may not generalize to multimodel ensembles, they can enhance the process understanding of emergent constraints and serve as out-of-sample tests of robustness.

Much of the global annual mean temperature change over Quaternary glacial cycles can be attributed to slow ice sheet and greenhouse gas feedbacks, but analysis of the short-term response to orbital forcings has the potential to reveal key relationships in the climate system. In particular, obliquity and precession both produce highly seasonal temperature responses at high latitudes. Here, idealized single-forcing model experiments are used to quantify Earth’s response to obliquity, precession, CO2, and ice sheets, and a linear reconstruction methodology is used to compare these responses to long proxy records around the globe. This comparison reveals mismatches between the annual mean response to obliquity and precession in models versus the signals within Antarctic ice cores. Weighting the model-based reconstruction toward austral winter or spring reduces these discrepancies, providing evidence for a seasonal bias in ice cores., Periodic changes in the tilt of the Earth’s axis alter the distribution of incoming solar radiation. Here, the authors show that the temperature response to this forcing seemingly differs in models and Antarctic ice cores, with a better agreement reached if ice cores are recording a seasonally weighted signal.

Author(s): Waibel, MS; Hulbe, CL; Jackson, CS; Martin, DF | Abstract: Rapid change now underway on Thwaites Glacier (TG) raises concern that a threshold for unstoppable grounding line retreat has been or is about to be crossed. We use a high-resolution ice sheet model to examine the mechanics of TG self-sustained retreat by nudging the grounding line just past the point of instability. We find that by modifying surface slope in the region of the grounding line, the rate of the forcing dictates the rate of retreat, even after the external forcing is removed. Grounding line retreats that begin faster proceed more rapidly because the shorter time interval for the grounding line to erode into the grounded ice sheet means relatively thicker ice and larger driving stress upstream of the boundary. Retreat is sensitive to short-duration re-advances associated with reduced external forcing where the bathymetry allows regrounding, even when an instability is invoked.

The authors present a polynomial chaos (PC)–based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers; while the posteriors of the remaining parameters were barely informative.

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.

The long-term climate variations of the Quaternary were primarily influenced by concurrent changes in Earth’s orbit, greenhouse gases, and ice sheets. However, because climate changes over the coming century will largely be driven by changes in greenhouse gases alone, it is important to better understand the separate contributions of each of these forcings in the past. To investigate this, idealized equilibrium simulations are conducted in which the climate is driven by separate changes in obliquity, precession, CO2, and ice sheets. To test the linearity of past climate change, anomalies from these single-forcing experiments are scaled and summed to compute linear reconstructions of past climate, which are then compared to mid-Holocene and last glacial maximum (LGM) snapshot simulations, where all forcings are applied together, as well as proxy climate records. This comparison shows that much of the climate response may be approximated as a linear response to forcings, while some features, such as modeled changes in sea ice and Atlantic meridional overturning circulation (AMOC), appear to be heavily influenced by nonlinearities. In regions where the linear reconstructions replicate the full-forcing experiments well, this analysis can help identify how each forcing contributes to the climate response. Monsoons at the mid-Holocene respond strongly to precession, while LGM monsoons are heavily influenced by the altered greenhouse gases and ice sheets. Contrary to previous studies, ice sheets produce pronounced tropical cooling at the LGM. Compared to proxy temperature records, the linear reconstructions replicate long-term changes well and also show which climate variations are not easily explained as direct responses to long-term forcings.

In this article we propose a novel non-parametric sampling approach to estimate posterior distributions from parameters of interest. Starting from an initial sample over the parameter space, this method makes use of this initial information to form a geometrical structure known as Voronoi tessellation over the whole parameter space. This rough approximation to the posterior distribution provides a way to generate new points from the posterior distribution without any additional costly model evaluations. By using a traditional Markov Chain Monte Carlo (MCMC) over the non-parametric tessellation, the initial approximate distribution is refined sequentially. We applied this method to a couple of climate models to show that this hybrid scheme successfully approximates the posterior distribution of the model parameters.

Climate data is highly correlated through the physics and dynamics of the atmosphere. Model evaluation often involves averages of various quantities over different regions and seasons making it difficult from a statistical perspective to quantify the significance of differences that arise between a model and observations. Here we present a strategy that makes use of a set of perfect modeling experiments to quantify the effects of these correlations on model evaluation metrics. This information is incorporated into Bayesian inference through a precision parameter with informative priors. These concepts are illustrated through an example of fitting a line through data that includes either uncorrelated or correlated noise as well as to the calibration of CAM3.1. The concept of a precision parameter may be applied as a strategy to weight different climate model evaluation metrics within a multivariate normal framework. From the example with CAM3.1, the precision parameter plays a central role in rescaling the estimated parametric uncertainties to better accommodate modeling structural errors.

Near the South Pole, a large subglacial lake exists beneath the East Antarctic Ice Sheet less than 10 km from where the bed temperature is inferred to be −9°C. A thermodynamic model was used to investigate the apparent contradiction of basal water existing in the vicinity of a cold bed. Model results indicate that South Pole Lake is freezing and that neither present-day geothermal flux nor ice flow is capable of producing the necessary heat to sustain basal water at this location. We hypothesize that the lake comprises relict water formed during a different configuration of ice dynamics when significant frictional heating from ice sliding was available. Additional modelling of assumed basal sliding shows frictional heating was capable of producing the necessary heat to fill South Pole Lake. Independent evidence of englacial structures measured by airborne radar revel ice-sheet flow was more dynamic in the past. Ice sliding is estimated to have ceased between 16.8 and 10.7 ka based on an ice chronology from a nearby borehole. These findings reveal major post-Last Glacial Maximum ice-dynamic change within the interior of East Antarctica, demonstrating that the present interior ice flow is different than that under full glacial conditions.

Author(s): Ng, Esmond; Evans, Katherine J; Caldwell, Peter; Hoffman, Forrest M; Jackson, Charles; Kerstin, Van Dam; Leung, Ruby; Martin, Daniel F; Ostrouchov, George; Tuminaro, Raymond; Ullrich, Paul; Wild, S; Williams, Samuel | Abstract: This report presents results from the DOE-sponsored workshop titled, Advancing X-Cutting Ideas for Computational Climate Science Workshop,'' known as AXICCS, held on September 12--13, 2016 in Rockville, MD. The workshop brought together experts in climate science, computational climate science, computer science, and mathematics to discuss interesting but unsolved science questions regarding climate modeling and simulation, promoted collaboration among the diverse scientists in attendance, and brainstormed about possible tools and capabilities that could be developed to help address them. Emerged from discussions at the workshop were several research opportunities that the group felt could advance climate science significantly. These include (1) process-resolving models to provide insight into important processes and features of interest and inform the development of advanced physical parameterizations, (2) a community effort to develop and provide integrated model credibility, (3) including, organizing, and managing increasingly connected model components that increase model fidelity yet complexity, and (4) treating Earth system models as one interconnected organism without numerical or data based boundaries that limit interactions. The group also identified several cross-cutting advances in mathematics, computer science, and computational science that would be needed to enable one or more of these big ideas. It is critical to address the need for organized, verified, and optimized software, which enables the models to grow and continue to provide solutions in which the community can have confidence. Effectively utilizing the newest computer hardware enables simulation efficiency and the ability to handle output from increasingly complex and detailed models. This will be accomplished through hierarchical multiscale algorithms in tandem with new strategies for data handling, analysis, and storage. These big ideas and cross-cutting technologies for enabling breakthrough climate simulation advancements also need the "glue" of outreach and learning across the scientific domains to be successful. The workshop identified several strategies to allow productive, continuous engagement across those who have a broad knowledge of the various angles of the problem. Specific ideas to foster education and tools to make material progress were discussed. Examples include follow-on cross-cutting meetings that enable unstructured discussions of the types this workshop fostered. A concerted effort to recruit undergraduate and graduate students from all relevant domains and provide them experience, training, and networking across their immediate expertise is needed. This will broaden and expand their exposure to the future needs and solutions, and provide a pipeline of scientists with a diversity of knowledge and know-how. Providing real-world experience with subject matter experts from multiple angles may also motivate the students to attack these problems and even come up with the missing solutions.

Uncertainty in wind forcing has long hampered direct tests of ocean model output against observations for the purpose of refining the boundary layer K-Profile Parameterization (KPP) of oceanic vertical mixing. Considered here is a short-term metric that could be sensitive to the ways in which the KPP directly affects the adjustment of sea surface temperatures for a given change in wind stress. In particular a metric is developed based on the lagged correlation between the 2–6 day filtered wind stress and sea surface temperature. The metric is normalized by estimated observational and model uncertainties such that the significance of differences may be assessed. For this purpose multiple wind reanalysis products and their blended combinations were used to represent the range of forcing uncertainty, while perturbed KPP parameter model runs explore the sensitivity of the metric to the parameterization of vertical mixing. The correlation metric is sensitive to perturbations to most KPP parameters, in ways that accord with expectations, although only a few parameters show a sensitivity on the same order as the sensitivity to switching between wind products. This suggests that uncertainties in wind forcing continue to be a significant limitation for applying direct observational tests of KPP physics. Moreover, model correlations are biased high, suggesting that the model lacks or does not resolve sources of variability on the 2–6 day time scale.

Pacifi c decadal variability (PDV) causes widespread, persistent fl uctuations that affect climate, water resources, and fi sheries throughout the Pacifi c basin, yet the magnitude, frequency, and causes of PDV remain poorly constrained. Here we present an absolutely dated, subannually resolved, 446 yr stable oxygen isotope (δ 18 O) cave record of rainfall variability in Vanuatu (southern Pacifi c Ocean), a location that has a climate heavily infl uenced by the South Pacifi c Convergence Zone (SPCZ). The δ 18 O-based proxy rainfall record is dominated by changes in stalagmite δ 18 O that are large (~1‰), quasi-periodic (~50 yr period), and generally abrupt (within 5‐10 yr). These isotopic changes imply abrupt rainfall changes of as much as ~1.8 m per wet season, changes that can be ~2.5◊ larger than the 1976 C.E. shift in rainfall amount associated with a PDV phase switch. The Vanuatu record also shares little commonality with previously documented changes in the Intertropical Convergence Zone during the Little Ice Age or solar forcing. We conclude that multidecadal SPCZ variability is likely of an endogenous nature. Large, spontaneous, and low-frequency changes in SPCZ rainfall during the past 500 yr have important implications for the relative magnitude of natural PDV possible in the coming century.

Recent investigations of submillennial paleoceanographic variability have attempted to resolve high-frequency climate signals such as the El Nino Southern Oscillation (ENSO) using the population statistics of individual planktic foraminiferal δ18O analyses. This approach is complicated by the relatively short lifespan of individual foraminifers (~2–4 weeks) compared to the time represented by a typical marine sediment sample (~decades to millennia). Here, we investigate the uncertainty associated with individual foraminiferal analyses (IFA) through simulations on forward modeled δ18Ocarbonate. First, focusing on the Nino3.4 region of the tropical Pacific Ocean, a bootstrap Monte Carlo algorithm is developed to constrain the uncertainty on IFA-statistics. Subsequently, to test the sensitivity of IFA to changes in seasonal cycle amplitude, ENSO amplitude, and ENSO frequency, synthetic time series of δ18Ocarbonate with differing variability are constructed and tested with our algorithm. The probabilities of the IFA technique in detecting changes in ENSO amplitude and seasonal cycle amplitude (or a combination of both) for the surface ocean and thermocline at different locations in the tropical Pacific are quantified. We find that the uncertainty in the standard deviation is smaller than the range, that the IFA-signal is insensitive to ENSO frequency, and at certain locations the seasonal cycle may dominate ENSO. IFA sensitivity towards ENSO is highest at the central equatorial Pacific surface ocean and the eastern equatorial Pacific (EEP) thermocline whereas sensitivity towards the seasonal cycle is highest at the EEP surface ocean. Our results suggest that rigorous uncertainty quantification should become standard practice for accurately interpreting IFA-data.

Atmospheric, oceanic, and subglacial forcing scenarios from the Sea-level Response to Ice Sheet Evolution (SeaRISE) project are applied to six three-dimensional thermomechanical ice-sheet models to assess Antarctic ice sheet sensitivity over a 500 year timescale and to inform future modeling and field studies. Results indicate (i) growth with warming, except within low-latitude basins (where inland thickening is outpaced by marginal thinning); (ii) mass loss with enhanced sliding (with basins dominated by high driving stresses affected more than basins with low-surface-slope streaming ice); and (iii) mass loss with enhanced ice shelf melting (with changes in West Antarctica dominating the signal due to its marine setting and extensive ice shelves; cf. minimal impact in the Terre Adelie, George V, Oates, and Victoria Land region of East Antarctica). Ice loss due to dynamic changes associated with enhanced sliding and/or sub-shelf melting exceeds the gain due to increased precipitation. Furthermore, differences in results between and within basins as well as the controlling impact of sub-shelf melting on ice dynamics highlight the need for improved understanding of basal conditions, grounding-zone processes, ocean-ice interactions, and the numerical representation of all three.

We investigate the performance of the newest generation multi-model ensemble (MME) from the Coupled Model Intercomparison Project (CMIP5). We compare the ensemble to the previous generation models (CMIP3) as well as several single model ensembles (SMEs), which are constructed by varying components of single models. These SMEs range from ensembles where parameter uncertainties are sampled (perturbed physics ensembles) through to an ensemble where a number of the physical schemes are switched (multi-physics ensemble). We focus on assessing reliability against present-day climatology with rank histograms, but also investigate the effective degrees of freedom (EDoF) of the fields of variables which makes the statistical test of reliability more rigorous, and consider the distances between the observation and ensemble members. We find that the features of the CMIP5 rank histograms, of general reliability on broad scales, are consistent with those of CMIP3, suggesting a similar level of performance for present-day climatology. The spread of MMEs tends towards being “over-dispersed” rather than “under-dispersed”. In general, the SMEs examined tend towards insufficient dispersion and the rank histogram analysis identifies them as being statistically distinguishable from many of the observations. The EDoFs of the MMEs are generally greater than those of SMEs, suggesting that structural changes lead to a characteristically richer range of model behaviours than is obtained with parametric/physical-scheme-switching ensembles. For distance measures, the observations and models ensemble members are similarly spaced from each other for MMEs, whereas for the SMEs, the observations are generally well outside the ensemble. We suggest that multi-model ensembles should represent an important component of uncertainty analysis.

Ten ice-sheet models are used to study sensitivity of the Greenland and Antarctic ice sheets to prescribed changes of surface mass balance, sub-ice-shelf melting and basal sliding. Results exhibit a large range in projected contributions to sea-level change. In most cases, the ice volume above flotation lost is linearly dependent on the strength of the forcing. Combinations of forcings can be closely approximated by linearly summing the contributions from single forcing experiments, suggesting that nonlinear feedbacks are modest. Our models indicate that Greenland is more sensitive than Antarctica to likely atmospheric changes in temperature and precipitation, while Antarctica is more sensitive to increased ice-shelf basal melting. An experiment approximating the Intergovernmental Panel on Climate Change’s RCP8.5 scenario produces additional first-century contributions to sea level of 22.3 and 8.1 cm from Greenland and Antarctica, respectively, with a range among models of 62 and 14 cm, respectively. By 200 years, projections increase to 53.2 and 26.7 cm, respectively, with ranges of 79 and 43 cm. Linear interpolation of the sensitivity results closely approximates these projections, revealing the relative contributions of the individual forcings on the combined volume change and suggesting that total ice-sheet response to complicated forcings over 200 years can be linearized.

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.

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.

Milankovitch proposed that variations in the earth’s 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 model’s response conforms to Milankovitch’s 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°–2°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.

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.

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 104–106 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 d...

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 (10 3 -10 5 years). A compromise is usually made to rep- resent the climate system by models that are averaged over one or more spatial dimensions or by three- dimensional models that are limited to simulating par- ticular ''snapshots'' in time. We take advantage of the short equilibration time (310 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 sim- ulated 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, aph- elion 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 d 18 O record. Both dynamical and thermal effects contribute to the increases in snowfall during these periods, through increases in storm activity and the fraction of precipi- tation 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.

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°×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.

Results are presented testing the sensitivity of a two-layer model to changes in the Laurentide ice sheet’s geometry following the collapse of the Hudson Bay ice dome. Since the ice sheet is thought to induce cooling over the North Atlantic through its mechanical effect on atmospheric circulation, the model shows a surprising result in that the removal of the Hudson Bay ice dome led to a further cooling of 4°C over the North Atlantic. This finding suggests that fluctuations in ice-sheet topography could have contributed to the climate variability witnessed in the geologic record. Further study is needed to understand the mechanism behind these results.

The Sea-level Response to Ice Sheet Evolution (SeaRISE) effort explores the sensitivity of the current generation of ice sheet models to external forcing to gain insight into the potential future contribution to sea level from the Greenland and Antarctic ice sheets. All participating models simulated the ice sheet response to three types of external forcings: a change in oceanic condition, a warmer atmospheric environment, and enhanced basal lubrication. Here an analysis of the spatial response of the Greenland ice sheet is presented, and the impact of model physics and spin-up on the projections is explored. Although the modeled responses are not always homogeneous, consistent spatial trends emerge from the ensemble analysis, indicating distinct vulnerabilities of the Greenland ice sheet. There are clear response patterns associated with each forcing, and a similar mass loss at the full ice sheet scale will result in different mass losses at the regional scale, as well as distinct thickness changes over the ice sheet. All forcings lead to an increased mass loss for the coming centuries, with increased basal lubrication and warmer ocean conditions affecting mainly outlet glaciers, while the impacts of atmospheric forcings affect the whole ice sheet. Key Points Sensitivity study of Greenland to atmospheric, oceanic and subglacial forcings Each forcing result in a different regional thickness response All forcings lead to an increased mass loss for the coming centuries ©2013. American Geophysical Union. All Rights Reserved.

The ensemble Kalman filter (EnKF) (Evensen, 2009) has proven effective in quantifying uncertainty in a number of challenging dynamic, state estimation, or data assimilation, problems such as weather forecasting and ocean modeling. In these problems a high-dimensional state parameter is successively updated based on recurring physical observations, with the aid of a computationally demanding forward model that prop- agates the state from one time step to the next. More recently, the EnKF has proven effective in history matching in the petroleum engineering community (Evensen, 2009; Oliver and Chen, 2010). Such applications typically involve estimating large numbers of parameters, describing an oil reservoir, using data from production history that accumulate over time. Such history matching problems are especially challenging examples of computer model calibration since they involve a large number of model parameters as well as a computationally demanding forward model. More generally, computer model calibration combines physical observations with a computational model - a computer model - to estimate unknown parameters in the computer model. This paper explores how the EnKF can be used in computer model calibration problems, comparing it to other more common approaches, considering applications in climate and cosmology., Comment: 20 pages; 11 figures

The disordering transition of half a monolayer of chemisorbed oxygen on Ru(001) can be classified in the universality class of the two-dimensional Heisenberg model with face-centered anisotropy. Theoretical studies of this class suggest fluctuation-induced first-order-transition behavior. Experimental study of the O/Ru(001) system, on the other hand, reveals a continuous phase transition with exponents near those of the three-state Potts model. We have investigated this discrepancy via Monte Carlo simulations of the Heisenberg model using the histogram method to determine transition order, focusing on two possible resolutions of the discrepancy: (1) finite-size rounding might be expected to render the experimental transition apparently continuous even if it is actually first order; or (2) transition order might depend on the details of the interaction energies. Despite its seeming inconsistency with the standard understanding of universality, we have strong evidence for the latter explanation; there appears to be a parameter regime of second-order behavior within standard ferromagnetic Heisenberg symmetry. We delineate this breakdown of universality and speculate as to its mechanism.

[1] 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 Dansgaard-Oeschger 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.

Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantication such as earthquake epicenter location and climate projections. To quantify the uncer- tainties resulting from a range of plausible model congurations 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 e- 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.

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the storage and movement of water (including soil moisture, snow, evaporation, and runoff) after it falls to the ground as precipitation. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy. Hence LSMs have been developed to integrate the available information, including satellite observations, using powerful computers, in order to track water storage and redistribution. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. Recently, the models have begun to simulate groundwater storage. In this paper, we compare several possible approaches, and examine the pitfalls associated with trying to estimate aquifer parameters (such as porosity) that are required by the models. We find that explicit representation of groundwater, as opposed to the addition of deeper soil layers, considerably decreases the sensitivity of modeled terrestrial water storage to aquifer parameter choices. We also 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 harmonious set.

[1] 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 10–50% 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 0.96–1.0 that it rains lightly in the model, compared to values of 0.84–1.0 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 (0.12–0.2) than that in the model (

[1] 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.

[1] 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 modeledobservational differences are large, excluding those regions where internal variability is small. INDEX TERMS: 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); 3314 Meteorology and Atmospheric Dynamics: Convective processes; 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation; 3394 Meteorology and Atmospheric Dynamics: Instruments and techniques; KEYWORDS: climate prediction, skill scores, numerical modeling

[1] 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 “best fit” 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.

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.

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.