Member Ensemble Research Articles

Ocean circulation response to anthropogenic aerosol and greenhouse gas forcing in the CSIRO-Mk3.6 climate model

We use the CSIRO-Mk3.6 coupled climate model to examine the impact of anthropogenic aerosols (AAs) and long-lived greenhouse gases (GHGs) on aspects of the global ocean circulation. Focusing on the second half of the twentieth century, we compare multiple ten member ensembles of historical climate change, which are forced by different combinations of forcing agents; these different simulations enable us to separately diagnose the effects of changes in AAs and GHGs. We also compare ten member 21st century ensembles driven by Representative Concentration Pathways 4.5 (RCP4.5) and 8.5 (RCP8.5). To a large degree the pattern of change in the oceans due to the impact of AAs is similar to the effect of increasing GHGs, but of opposite sign. The Atlantic Meridional Overturning Circulation (and associated North Atlantic Deep Water formation) strengthens in response to historical changes in AAs but weakens in response to increasing GHGs. Similarly, the Indonesian Throughflow strengthens in response to AAs, and weakens in response to increasing GHGs. The Drake Passage Transport, however, shows a small weakening (strengthening) due to historical changes in AAs (GHGs) in the ensemble mean. The change in the Drake Passage Transport is much clearer in the 21st century, in which it increases strongly in response to increasing GHGs and decreasing AAs in both RCP4.5 and RCP8.5. The results suggests that without the influence of AAs, changes in ocean circulation would have already followed a path much more like one dominated by increasing GHGs. Considering that the AA levels are expected to decrease during the next few decades, the effects of increasing GHGs on ocean circulation will be amplified accordingly.

Combining probabilistic precipitation forecasts from a nowcasting technique with a time-lagged ensemble

A probabilistic nowcasting technique based on the Local Lagrangian method is combined with probabilistic forecasts derived from a time-lagged convection-permitting model to produce seamless short-term probabilistic precipitation forecasts. The fraction, the neighbourhood and the mean method are used to derive probabilistic information from this eight member ensemble. The model-based forecasts are calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts is evaluated with three quality measures. Probabilistic model-based forecasts are found to outperform probabilistic radar-based nowcasts after 2.25–3.5 h. Weighting functions derived in a lead time dependent evaluation of forecast skill are used to combine nowcasts and forecasts additively. The resulting seamless blended forecasts maintain or exceed the skill of the respective best component. In comparison with similar studies, the application of the time-lagged approach increases the skill of the numerical forecasts and hence the blended forecasts. Copyright © 2013 Royal Meteorological Society

On the estimation of systematic error in regression-based predictions of climate sensitivity

An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estimating the systematic uncertainty in predictions of climate sensitivity are dependent on arbitrary choices relating to ensemble sampling strategy. Using a constrained regression approach, a multivariate predictor may be derived based upon the mean climatic state of each ensemble member, but components of this predictor are excluded if they cannot be validated within the CMIP-3 ensemble. It is found that the application of the CMIP-3 constraint serves to decrease the upper bound of likelihood for climate sensitivity when compared with previous studies, with 10th and 90th percentiles of probability at 1.5 K and 4.3 K respectively.

Warm-season Thermodynamically-driven Rainfall Prediction with Support Vector Machines

Dynamic numerical weather prediction models have been designed to deal with large-scale, highly predictable midlatitude atmospheric patterns. However, the capability of these models to simulate thermodynamically driven warm-season rainfall events, such as afternoon airmass thunderstorm formation in subtropical summers, is highly limited. Current methods of addressing this issue have included ensemble numerical weather prediction simulations, where an ensemble mean of multiple simulations with varied model physics is used as an improved prediction over any individual ensemble member. These approaches still yield only modest skill primarily due to inherent biases in each ensemble member. As such, the current research will utilize machine learning to combine logically ensemble members into a single prediction of warm-season rainfall. In particular, a support vector machine classification scheme that employs members of a 30 member ensemble as predictors and observed rainfall patterns as a predictand will be formulated on multiple warm-season rainfall days in an effort to develop an improved prognosis of warm-season rainfall that can be implemented in operational meteorology forecasts. The primary goal of the work is to obtain a statistically significant improvement of predictive skill over currently utilized ensemble member approaches.

Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA

ABSTRACTThis study investigated the potential impact of soil moisture perturbations on the statistical spread of an ensemble forecast for three different synoptic events during the summer of 2006. Soil moisture was perturbed from a control simulation to generate a 12 member ensemble with six drier and six moister soils. The impacts on the near-surface atmospheric conditions and on precipitation were analysed. It was found, as previous studies have confirmed, that soil moisture can change the spatial and temporal distribution of precipitation and of the overlying circulation. It was found that regardless of the conditions in synoptic forcing, temperature, relative humidity and horizontal wind field exhibited a spatial correlation coefficient (R) close to one with respect to the control simulation. Vertical velocity, however, showed a marked decrease in R down to 0.4 as the precipitation activity increased. For vertical velocity, however, this quantity grew to near 1.0 consistent with R near zero and standard deviations very close to that of the control. These results suggested a more complex picture in which soil moisture perturbations played a major role in modifying precipitation and the near-surface circulation but did not broaden the statistical spread of trajectories in phase space of all variables.

Ensembles to model the impact of urbanization for a summertime rainstorm process in Yangtze River Delta, China

AbstractThe impact of urbanization for a summertime rainstorm process in the Yangtze River Delta was investigated based on two 96 member ensembles using the Weather Research and Forecasting (WRF) model. Results show that the increase of precipitation mainly occurs downwind of the city belt in this region. The increase and decrease of precipitation forms two zigzag belts in the precipitation area. The t‐test illustrates that the discrepancies of near‐surface environment around the city belt, the precipitation downwind of the city belt as well as the atmospheric variables in the lower troposphere over the rain belt, caused by urbanization, is significant in such a short‐term rainstorm process. Copyright © 2012 Royal Meteorological Society

Reliability of decadal predictions

The reliability of multi‐year predictions of climate is assessed using probabilistic Attributes Diagrams for near‐surface air temperature and sea surface temperature, based on 54 member ensembles of initialised decadal hindcasts using the ECMWF coupled model. It is shown that the reliability from the ensemble system is good over global land areas, Europe and Africa and for the North Atlantic, Indian Ocean and, to a lesser extent, North Pacific basins for lead times up to 6–9 years. North Atlantic SSTs are reliably predicted even when the climate trend is removed, consistent with the known predictability for this region. By contrast, reliability in the Indian Ocean, where external forcing accounts for most of the variability, deteriorates severely after detrending. More conventional measures of forecast quality, such as the anomaly correlation coefficient (ACC) of the ensemble mean, are also considered, showing that the ensemble has significant skill in predicting multi‐annual temperature averages.

Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48-h forecasts at 7 km horizontal resolution were generated for a 2000×2000 km domain covering central Europe. Forecasts were made for seven case studies characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

SENSITIVITY ANALYSIS OF NONLINEAR MODELS TO PARAMETER PERTURBATIONS FOR SMALL SIZE ENSEMBLES OF MODEL OUTPUTS

A new technique for nonlinear sensitivity analysis of geophysical models for small size ensembles of model outputs has been developed. Such an analysis utilizes the following metrics: (a) Sobol–Saltelli sensitivity indices and cumulative distribution functions if perturbations of model parameters are random, and (b) a Hartley-like measure if perturbations of model parameters are nonrandom and parametrized through fuzzy sets. The indices and the Hartley-like measure allow for ranging model parameters along their significance to the model output. Our calculations demonstrate that accurate estimates of the sensitivity indices are possible even if an ensemble of random perturbations contains considerably less than 100 members. Some calculations were successfully provided for random ensembles with 20–30 members only but, in general 50–100 member ensembles are required to get robust and significant estimations of model sensitivity. The fuzzy set concept allows for robust estimations for small size nonrandom ensembles of model outputs (50–100 members) and accounts for additional a priori information on model sensitivity coming from different sources. The Lorenz 63 model (a few degrees of freedom) and the ocean component (POP) of the Community Climate System Model (CCSM3) (several thousand degrees of freedom) are used to illustrate the sensitivity analysis based on this approach.

Global Warming Effects on U.S. Hurricane Damage

Abstract While many studies of the effects of global warming on hurricanes predict an increase in various metrics of Atlantic basin-wide activity, it is less clear that this signal will emerge from background noise in measures of hurricane damage, which depend largely on rare, high-intensity landfalling events and are thus highly volatile compared to basin-wide storm metrics. Using a recently developed hurricane synthesizer driven by large-scale meteorological variables derived from global climate models, 1000 artificial 100-yr time series of Atlantic hurricanes that make landfall along the U.S. Gulf and East Coasts are generated for four climate models and for current climate conditions as well as for the warmer climate of 100 yr hence under the Intergovernmental Panel on Climate Change (IPCC) emissions scenario A1b. These synthetic hurricanes damage a portfolio of insured property according to an aggregate wind-damage function; damage from flooding is not considered here. Assuming that the hurricane climate changes linearly with time, a 1000-member ensemble of time series of property damage was created. Three of the four climate models used produce increasing damage with time, with the global warming signal emerging on time scales of 40, 113, and 170 yr, respectively. It is pointed out, however, that probabilities of damage increase significantly well before such emergence time scales and it is shown that probability density distributions of aggregate damage become appreciably separated from those of the control climate on time scales as short as 25 yr. For the fourth climate model, damages decrease with time, but the signal is weak.

Modeling the 8.2 ka event using a coupled atmosphere–ocean GCM

A series of simulations have been performed using the Hadley Centre GCM, (HadCM3) to investigate the 8.2 ka event. A control simulation with 9 ka boundary conditions showed that intrinsic variability in the strength of the N. Atlantic Meridional Overturning Circulation (MOC) was greater than in a comparable pre-industrial simulation but these natural variations were not strong enough to replicate the 8.2 ka event at any time, consistent with the idea that the event was ‘forced’ through a freshwater discharge. We therefore investigated the response of the 9 ka climate to a freshwater forcing consistent with the final drainage of Lake Agassiz. A 10 member ensemble was performed with 5 Sv of freshwater added to the N. Atlantic ocean for 1 year at various times of the control run, which led to a reduction in the strength of the MOC and the temperature, precipitation and δ 18O of precipitation over the N. Atlantic region. The strength and location of the response agreed well with proxy evidence of the 8.2 ka event but the duration was too short in all cases. The response to the freshwater forcing was sensitive to the initial state of the ocean, with better agreement with paleodata when the freshwater was added at a time when the Atlantic MOC was relatively weak. The structure and duration of the δ 18O p response was consistent over a large region, and appeared in better agreement with regional precipitation than regional temperature, however there were spatial differences between the maximum δ 18O p response and both the maximum temperature and precipitation responses.

Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses

Abstract Ensemble Kalman filter (EnKF) techniques have been proposed for obtaining atmospheric state estimates on the scale of individual convective storms from radar and other observations, but tests of these methods with observations of real convective storms are still very limited. In the current study, radar observations of the 8 May 2003 Oklahoma City tornadic supercell thunderstorm were assimilated into the National Severe Storms Laboratory (NSSL) Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) with an EnKF method. The cloud model employed 1-km horizontal grid spacing, a single-moment bulk precipitation-microphysics scheme, and a base state initialized with sounding data. A 50-member ensemble was produced by randomly perturbing base-state wind profiles and by regularly adding random local perturbations to the horizontal wind, temperature, and water vapor fields in and near observed precipitation. In a reference experiment, only Doppler-velocity observations were assimilated into the NCOMMAS ensemble. Then, radar-reflectivity observations were assimilated together with Doppler-velocity observations in subsequent experiments. Influences that reflectivity observations have on storm-scale analyses were revealed through parameter-space experiments by varying observation availability, observation errors, ensemble spread, and choices for what model variables were updated when a reflectivity observation was assimilated. All experiments produced realistic storm-scale analyses that compared favorably with independent radar observations. Convective storms in the NCOMMAS ensemble developed more quickly when reflectivity observations and velocity observations were both assimilated rather than only velocity, presumably because the EnKF utilized covariances between reflectivity and unobserved model fields such as cloud water and vertical velocity in efficiently developing realistic storm features. Recurring spatial patterns in the differences between predicted and observed reflectivity were noted particularly at low levels, downshear of the supercell’s updraft, in the anvil of moderate-to-light precipitation, where reflectivity in the model was typically lower than observed. Bias errors in the predicted rain mixing ratios and/or the size distributions that the bulk scheme associates with these mixing ratios are likely responsible for this reflectivity underprediction. When a reflectivity observation is assimilated, bias errors in the model fields associated with reflectivity (rain, snow, and hail–graupel) can be projected into other model variables through the ensemble covariances. In the current study, temperature analyses in the downshear anvil at low levels, where reflectivity was underpredicted, were very sensitive both to details of the assimilation algorithm and to ensemble spread in temperature. This strong sensitivity suggests low confidence in analyses of low-level cold pools obtained through reflectivity-data assimilation.

PREDICTABILITY OF THE HEAVY RAINFALL DISASTER AT KANI CITY IN JULY 2010 BASED ON MODEL ENSEMBLE FORECASTS

The predictability of the heavy rainfall disaster at Kani City, Japan in July 2010 is investigated using a model ensemble approach which relies on the combination of 32 member ensembles obtained from multiple parameterizations in the mesoscale meteorological model PSU/NCAR MM5. The model is initialized using the 20-km resolution global analyses JMA GSM-GPV with and without the radar-data assimilation system developed by Yoshino et al. (2009). It is found that the multi-parameterization ensemble forecasts averaged by 32 members during 24 hours are very skillful in indicating the area of severe local storm activity, while each member deviates largely from the ensemble mean. Furthermore, the radar-assimilated ensemble forecasts enable us to more precisely predict the area of greatest rainfall potential, because the initial conditions used here realistically represent 3-dimensional distributions of cloud microphysical properties in the atmosphere.

Can ensembles of regional climate model simulations improve results from sensitivity studies?

Intrinsic variability (IV) in regional climate models (RCMs) is often assumed to be small because at climatological timescales, the model solutions tend to be dominated by the model’s lateral boundary conditions. Recent studies have indicated that this IV may actually be large in certain instances for some variables. Direct interpretation of anomalies from RCM sensitivity studies relies on the assumption that differences between model simulations are entirely due to a physical forcing. However, if IV is as large or larger than the physical signal, then this assumption is violated. Using a 20 member ensemble of RCM simulations, we verify that IV of precipitation within a RCM can be large enough to violate the sensitivity study assumption, and we show that generating ensembles of simulations can help reduce the level of IV. We also present two indicators that can rule out the influence of IV when it is ambiguous whether anomalies within a sensitivity study are due to the sensitivity perturbation or whether they are due to IV.

Changes in wind gust extremes over Central Europe derived from a small ensemble of high resolution regional climate models

Simulations of the regional climate models CCLM and REMO, forced by different runs of the global model ECHAM5, are used to quantify changes in the local winter gust extremes over the next decades. The horizontal resolution of the models is smaller than 20 km. The gust wind speed conditions are characterised through the comparison of gusts in the control (1971-2000) and the projection period (2021-2050) for the SRES scenarios A1B, A2 and B 1. Extreme value statistics is applied to calculate gust wind speeds for different return periods at each grid point. The results show that the individual regional model runs differ considerably in the change signal and the significance of the single simulations is limited to some main features. The spatial variability of the gusts is only marginally influenced by the return period, but the significance decrease with increasing return period. However, the global model determines the general features of the change signal in the individual regional simulations. Therefore a seven member ensemble provides the opportunity to study the main features of the changes in the gust wind speeds. Over Northern Germany, the change between the two periods in wind gust speed for a 10-year return period studied was found to lie between +6 % and -1.5 % with median, 25th and 75th percentile of 1.5 %, 0.7 % and 2.8 %, respectively. This is confirmed by the majority of the model runs. Over the rest of Germany, the results are not uniform. Nevertheless, over some parts over Southern and Central Germany the majority of the simulations projects a slight decrease of gust wind speeds. According to these results, the current high level of storm activity will not substantially drop until 2050 and will even rise in Northern Germany.

Role of tropical Pacific SSTs in global medieval hydroclimate: A modeling study

The role of tropical Pacific SSTs in driving global medieval hydroclimate is assessed. Using fossil coral records from Palmyra Atoll, tropical Pacific sea surface temperature (SST) boundary conditions are derived for the period 1320–1462 A.D. These boundary conditions consist of La Niña‐like mean state conditions in the tropical Pacific with inter‐annual and decadal variability about that altered state. The reconstructed SSTs in the tropical Pacific are used to force a 16 member ensemble of atmospheric general circulation model (AGCM) simulations, coupled to a one layer ocean model outside of the tropical Pacific. The AGCM simulations of medieval climate are compared with modern climate simulations for the period 1856–2005 A.D. and are shown to reproduce many aspects of medieval hydroclimate found in paleo‐proxy records for much of the Western Hemisphere, northern Eurasia, and the northern tropics. These results suggest that many features of global medieval hydroclimate changes can be explained by changes in tropical Pacific SSTs, though the potential role for other oceans is also discussed.

Application of Proteomic Marker Ensembles to Subcellular Organelle Identification

Compartmentalization of biological processes and the associated cellular components is crucial for cell function. Typically, the location of a component is revealed through a co-localization and/or co-purification with an organelle marker. Therefore, the identification of reliable markers is critical for a thorough understanding of cellular function and dysfunction. We fractionated macrophage-like RAW264.7 cells, both in the resting and endotoxin-activated states, into six fractions representing the major organelles/compartments: nuclei, mitochondria, cytoplasm, endoplasmic reticulum, and plasma membrane as well as an additional dense microsomal fraction. The identity of the first five of these fractions was confirmed via the distribution of conventional enzymatic markers. Through a quantitative liquid chromatography/mass spectrometry-based proteomics analysis of the fractions, we identified 50-member ensembles of marker proteins ("marker ensembles") specific for each of the corresponding organelles/compartments. Our analysis attributed 206 of the 250 marker proteins ( approximately 82%) to organelles that are consistent with the location annotations in the public domain (obtained using DAVID 2008, EntrezGene, Swiss-Prot, and references therein). Moreover, we were able to correct locations for a subset of the remaining proteins, thus proving the superior power of analysis using multiple organelles as compared with an analysis using one specific organelle. The marker ensembles were used to calculate the organelle composition of the six above mentioned subcellular fractions. Knowledge of the precise composition of these fractions can be used to calculate the levels of metabolites in the pure organelles. As a proof of principle, we applied these calculations to known mitochondria-specific lipids (cardiolipins and ubiquinones) and demonstrated their exclusive mitochondrial location. We speculate that the organelle-specific protein ensembles may be used to systematically redefine originally morphologically defined organelles as biochemical entities.

Seasonal Atlantic tropical cyclone hindcasting/forecasting using two sea surface temperature datasets

Seasonal Atlantic basin tropical cyclone hindcasts are conducted from 1986–2005 using the Florida State University/Center for Ocean Atmospheric Prediction Studies atmospheric global spectral model and two sea surface temperature products. The two sea surface temperature products are: the National Oceanic and Atmospheric Administrations Climate Forecast System Model's 1 June forecast and the weekly observed sea surface temperatures. The hindcasts extend through the 180‐day Atlantic hurricane season. A four member ensemble is generated for each year using time‐lagged atmospheric initial conditions centered on 1 June of the respective year. The interannual variability of total number of Atlantic storms (tropical storms plus hurricanes) and accumulated cyclone energy using both sea surface temperature products yield high correlations which are statistically significant, although using the observed sea surface temperature product yields higher correlations. The results indicates that the Climate Forecast System Model's sea surface temperatures are suitable for use in real‐time seasonal tropical cyclone forecasting in the Atlantic basin in a two‐tiered approach.

Soil-precipitation feedbacks during the South American Monsoon as simulated by a regional climate model

We summarize the recent progress in regional climate modeling in South America with the Rossby Centre regional atmospheric climate model (RCA3-E), with emphasis on soil moisture processes. A series of climatological integrations using a continental scale domain nested in reanalysis data were carried out for the initial and mature stages of the South American Monsoon System (SAMS) of 1993- 92 and were analyzed on seasonal and monthly timescales. The role of including a spatially varying soil depth, which extends to 8 m in tropical forest, was evaluated against the standard constant soil depth of the model of about 2 m, through two five member ensemble simulations. The influence of the soil depth was relatively weak, with both beneficial and detrimental effects on the simulation of the seasonal mean rainfall. Secondly, two ensembles that differ in their initial state of soil moisture were prepared to study the influence of anomalously dry and wet soil moisture initial conditions on the intraseasonal development of the SAMS. In these simulations the austral winter soil moisture initial condition has a strong influence on wet season rainfall over feed back upon the monsoon, not only over the Amazon region but

Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs

The use of radiative kernels to diagnose climate feedbacks is a recent development that may be applied to existing climate change simulations. We apply the radiative kernel technique to transient simulations from a multi-thousand member perturbed physics ensemble of coupled atmosphere-ocean general circulation models, comparing distributions of model feedbacks with those taken from the CMIP-3 multi GCM ensemble. Although the range of clear sky longwave feedbacks in the perturbed physics ensemble is similar to that seen in the multi-GCM ensemble, the kernel technique underestimates the net clear-sky feedbacks (or the radiative forcing) in some perturbed models with significantly altered humidity distributions. In addition, the compensating relationship between global mean atmospheric lapse rate feedback and water vapor feedback is found to hold in the perturbed physics ensemble, but large differences in relative humidity distributions in the ensemble prevent the compensation from holding at a regional scale. Both ensembles show a similar range of response of global mean net cloud feedback, but the mean of the perturbed physics ensemble is shifted towards more positive values such that none of the perturbed models exhibit a net negative cloud feedback. The perturbed physics ensemble contains fewer models with strong negative shortwave cloud feedbacks and has stronger compensating positive longwave feedbacks. A principal component analysis used to identify dominant modes of feedback variation reveals that the perturbed physics ensemble produces very different modes of climate response to the multi-model ensemble, suggesting that one may not be used as an analog for the other in estimates of uncertainty in future response. Whereas in the multi-model ensemble, the first order variation in cloud feedbacks shows compensation between longwave and shortwave components, in the perturbed physics ensemble the shortwave feedbacks are uncompensated, possibly explaining the larger range of climate sensitivities observed in the perturbed simulations. Regression analysis suggests that the parameters governing cloud formation, convection strength and ice fall speed are the most significant in altering climate feedbacks. Perturbations of oceanic and sulfur cycle parameters have relatively little effect on the atmospheric feedbacks diagnosed by the kernel technique.