Multi-site evaluation of an urban land-surface model: intra-urban heterogeneity, seasonality and parameter complexity requirements

An extensive off-line evaluation of the Noah/Single Layer Urban Canopy Model (Noah/SLUCM) urban land-surface model is presented using data from 15 sites to assess (1) the ability of the scheme to reproduce the surface energy balance observed in a range of urban environments, including seasonal changes, and (2) the impact of increasing complexity of input parameter information. Model performance is found to be most dependent on representation of vegetated surface area cover; refinement of other parameter values leads to smaller improvements. Model biases in net all-wave radiation and trade-offs between turbulent heat fluxes are highlighted using an optimization algorithm. Here we use the Urban Zones to characterize Energy partitioning (UZE) as the basis to assign default SLUCM parameter values. A methodology (FRAISE) to assign sites (or areas) to one of these categories based on surface characteristics is evaluated. Using three urban sites from the Basel Urban Boundary Layer Experiment (BUBBLE) dataset, an independent evaluation of the model performance with the parameter values representative of each class is performed. The scheme copes well with both seasonal changes in the surface characteristics and intra-urban heterogeneities in energy flux partitioning, with RMSE performance comparable to similar state-of-the-art models for all fluxes, sites and seasons. The potential of the methodology for high-resolution atmospheric modelling application using the Weather Research and Forecasting (WRF) model is highlighted. This analysis supports the recommendations that (1) three classes are appropriate to characterize the urban environment, and (2) that the parameter values identified should be adopted as default values in WRF.

Improving the evaluation of hydrological multi-model forecast performance in the Upper Danube Catchment

Medium range flood forecasting activities, driven by various meteorological forecasts ranging from high resolution deterministic forecasts to low spatial resolution ensemble prediction systems, share a major challenge in the appropriateness and design of performance measures. In this paper possible limitations of some traditional hydrological and meteorological prediction quality and verification measures are identified. Some simple modifications are applied in order to circumvent the problem of the autocorrelation dominating river discharge time-series and in order to create a benchmark model enabling the decision makers to evaluate the forecast quality and the model quality. Although the performance period is quite short the advantage of a simple cost-loss function as a measure of forecast quality can be demonstrated.

A multi-model analysis of the role of the ocean on the African and Indian monsoon during mid-Holocene

We investigate the role of the ocean feedback on the climate in response to insolation forcing during the mid-Holocene (6,000 year BP) using results from seven coupled ocean–atmosphere general circulation models. We examine how the dipole in late summer sea-surface temperature (SST) anomalies in the tropical Atlantic increases the length of the African monsoon, how this dipole structure is created and maintained, and how the late summer SST warming in the northwest Indian Ocean affects the monsoon retreat in this sector. Similar mechanisms are found in all of the models, including a strong wind evaporation feedback and changes in the mixed layer depth that enhance the insolation forcing, as well as increased Ekman transport in the Atlantic that sharpens the Atlantic dipole pattern. We also consider changes in interannual variability over West Africa and the Indian Ocean. The teleconnection between variations in SST and Sahelian precipitation favor a larger impact of the Atlantic dipole mode in this region. In the Indian Ocean, the strengthening of the Indian dipole structure in autumn has a damping effect on the Indian dipole mode at the interannual time scale

Multi-scale sensible heat fluxes in the urban environment from large aperture scintillometry and eddy covariance

Sensible heat fluxes (QH) are determined using scintillometry and eddy covariance over a suburban area. Two large aperture scintillometers provide spatially integrated fluxes across path lengths of 2.8 km and 5.5 km over Swindon, UK. The shorter scintillometer path spans newly built residential areas and has an approximate source area of 2-4 km2, whilst the long path extends from the rural outskirts to the town centre and has a source area of around 5-10 km2. These large-scale heat fluxes are compared with local-scale eddy covariance measurements. Clear seasonal trends are revealed by the long duration of this dataset and variability in monthly QH is related to the meteorological conditions. At shorter time scales the response of QH to solar radiation often gives rise to close agreement between the measurements, but during times of rapidly changing cloud cover spatial differences in the net radiation (Q*) coincide with greater differences between heat fluxes. For clear days QH lags Q*, thus the ratio of QH to Q* increases throughout the day. In summer the observed energy partitioning is related to the vegetation fraction through use of a footprint model. The results demonstrate the value of scintillometry for integrating surface heterogeneity and offer improved understanding of the influence of anthropogenic materials on surface-atmosphere interactions.

Predictability of the western North Pacific summer climate associated with different ENSO phases by ENSEMBLES multi-model seasonal forecasts

Predictability of the western North Pacific (WNP) summer climate associated with different El Niño–Southern Oscillation (ENSO) phases is investigated in this study based on the 1-month lead retrospective forecasts of five state-of-the-art coupled models from ENSEMBLES. During the period from 1960 to 2005, the models well capture the WNP summer climate anomalies during most of years in different ENSO phases except the La Niña decaying summers. In the El Niño developing, El Niño decaying and La Niña developing summers, the prediction skills are high for the WNP summer monsoon index (WNPMI), with the prediction correlation larger than 0.7. The high prediction skills of the lower-tropospheric circulation during these phases are found mainly over the tropical western Pacific Ocean, South China Sea and subtropical WNP. These good predictions correspond well to their close teleconnection with ENSO and the high prediction skills of tropical SSTs. By contrast, for the La Niña decaying summers, the prediction skills are considerably low with the prediction correlation for the WNPMI near to zero and low prediction skills around the Philippines and subtropical WNP. These poor predictions relate to the weak summer anomalies of the WNPMI during the La Niña decaying years and no significant connections between the WNP lower-tropospheric circulation anomalies and the SSTs over the tropical central and eastern Pacific Ocean in observations. However, the models tend to predict an apparent anomalous cyclone over the WNP during the La Niña decaying years, indicating a linearity of the circulation response over WNP in the models prediction in comparison with that during the El Niño decaying years which differs from observations. In addition, the models show considerable capability in describing the WNP summer anomalies during the ENSO neutral summers. These anomalies are related to the positive feedback between the WNP lower-tropospheric circulation and the local SSTs. The models can capture this positive feedback but with some uncertainties from different ensemble members during the ENSO neutral summers.

Radiative forcing and climate metrics for ozone precursor emissions: the impact of multi-model averaging

Multi-model ensembles are frequently used to assess understanding of the response of ozone and methane lifetime to changes in emissions of ozone precursors such as NOx, VOCs (volatile organic compounds) and CO. When these ozone changes are used to calculate radiative forcing (RF) (and climate metrics such as the global warming potential (GWP) and global temperature-change potential (GTP)) there is a methodological choice, determined partly by the available computing resources, as to whether the mean ozone (and methane) concentration changes are input to the radiation code, or whether each model's ozone and methane changes are used as input, with the average RF computed from the individual model RFs. We use data from the Task Force on Hemispheric Transport of Air Pollution source–receptor global chemical transport model ensemble to assess the impact of this choice for emission changes in four regions (East Asia, Europe, North America and South Asia). We conclude that using the multi-model mean ozone and methane responses is accurate for calculating the mean RF, with differences up to 0.6% for CO, 0.7% for VOCs and 2% for NOx. Differences of up to 60% for NOx 7% for VOCs and 3% for CO are introduced into the 20 year GWP. The differences for the 20 year GTP are smaller than for the GWP for NOx, and similar for the other species. However, estimates of the standard deviation calculated from the ensemble-mean input fields (where the standard deviation at each point on the model grid is added to or subtracted from the mean field) are almost always substantially larger in RF, GWP and GTP metrics than the true standard deviation, and can be larger than the model range for short-lived ozone RF, and for the 20 and 100 year GWP and 100 year GTP. The order of averaging has most impact on the metrics for NOx, as the net values for these quantities is the residual of the sum of terms of opposing signs. For example, the standard deviation for the 20 year GWP is 2–3 times larger using the ensemble-mean fields than using the individual models to calculate the RF. The source of this effect is largely due to the construction of the input ozone fields, which overestimate the true ensemble spread. Hence, while the average of multi-model fields are normally appropriate for calculating mean RF, GWP and GTP, they are not a reliable method for calculating the uncertainty in these fields, and in general overestimate the uncertainty.

Serial clustering of extratropical cyclones in a multi-model ensemble of historical and future simulations

This study has investigated serial (temporal) clustering of extra-tropical cyclones simulated by 17 climate models that participated in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December-February counts of Atlantic storm tracks passing nearby each grid point. Results from single historical simulations of 1975-2005 were compared to those from historical ERA40 reanalyses from 1958-2001 ERA40 and single future model projections of 2069-2099 under the RCP4.5 climate change scenario. Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability. Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30 year sample, is prone to large amounts of sampling uncertainty that obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion that are discernible above natural sampling variations.

A multi-model evaluation of aerosols over South Asia: common problems and possible causes

Atmospheric pollution over South Asia attracts special attention due to its effects on regional climate, water cycle and human health. These effects are potentially growing owing to rising trends of anthropogenic aerosol emissions. In this study, the spatio-temporal aerosol distributions over South Asia from seven global aerosol models are evaluated against aerosol retrievals from NASA satellite sensors and ground-based measurements for the period of 2000–2007. Overall, substantial underestimations of aerosol loading over South Asia are found systematically in most model simulations. Averaged over the entire South Asia, the annual mean aerosol optical depth (AOD) is underestimated by a range 15 to 44% across models compared to MISR (Multi-angle Imaging SpectroRadiometer), which is the lowest bound among various satellite AOD retrievals (from MISR, SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra). In particular during the post-monsoon and wintertime periods (i.e., October–January), when agricultural waste burning and anthropogenic emissions dominate, models fail to capture AOD and aerosol absorption optical depth (AAOD) over the Indo–Gangetic Plain (IGP) compared to ground-based Aerosol Robotic Network (AERONET) sunphotometer measurements. The underestimations of aerosol loading in models generally occur in the lower troposphere (below 2 km) based on the comparisons of aerosol extinction profiles calculated by the models with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Furthermore, surface concentrations of all aerosol components (sulfate, nitrate, organic aerosol (OA) and black carbon (BC)) from the models are found much lower than in situ measurements in winter. Several possible causes for these common problems of underestimating aerosols in models during the post-monsoon and wintertime periods are identified: the aerosol hygroscopic growth and formation of secondary inorganic aerosol are suppressed in the models because relative humidity (RH) is biased far too low in the boundary layer and thus foggy conditions are poorly represented in current models, the nitrate aerosol is either missing or inadequately accounted for, and emissions from agricultural waste burning and biofuel usage are too low in the emission inventories. These common problems and possible causes found in multiple models point out directions for future model improvements in this important region.

Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble

A statistical-dynamical downscaling method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble are considered. The downscaling method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021–2060 and 2061–2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 ensemble mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an ensemble mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061–2100 compared to 2021–2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an ensemble of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model ensembles are needed to provide a better quantification and understanding of the future changes.

Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.

Fast and slow precipitation responses to individual climate forcers: a PDRMIP multi-model study

Precipitation is expected to respond differently to various drivers of anthropogenic climate change. We present the first results from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where nine global climate models have perturbed CO2, CH4, black carbon, sulfate, and solar insolation. We divide the resulting changes to global mean and regional precipitation into fast responses that scale with changes in atmospheric absorption and slow responses scaling with surface temperature change. While the overall features are broadly similar between models, we find significant regional intermodel variability, especially over land. Black carbon stands out as a component that may cause significant model diversity in predicted precipitation change. Processes linked to atmospheric absorption are less consistently modeled than those linked to top-of-atmosphere radiative forcing. We identify a number of land regions where the model ensemble consistently predicts that fast precipitation responses to climate perturbations dominate over the slow, temperature-driven responses.

GRASP heuristic with path-relinking for the multi-plant capacitated lot sizing problem

This paper addresses the independent multi-plant, multi-period, and multi-item capacitated lot sizing problem where transfers between the plants are allowed. This is an NP-hard combinatorial optimization problem and few solution methods have been proposed to solve it. We develop a GRASP (Greedy Randomized Adaptive Search Procedure) heuristic as well as a path-relinking intensification procedure to find cost-effective solutions for this problem. In addition, the proposed heuristics is used to solve some instances of the capacitated lot sizing problem with parallel machines. The results of the computational tests show that the proposed heuristics outperform other heuristics previously described in the literature. The results are confirmed by statistical tests. (C) 2009 Elsevier B.V. All rights reserved.

Equipping Simulation Model (BIOSIM)’s Actors With Multi-agent Intelligence on Cross platforms

Thesis is to Introduce an Intelligent cross platform architecture with Multi-agent system in order to equip the simulation Models with agents, having intelligent behavior, reactive and pro-active nature and rational in decision making.