scholarly journals Regional model simulations of New Zealand climate

1998 ◽  
Vol 103 (D6) ◽  
pp. 5973-5982 ◽  
Author(s):  
James A. Renwick ◽  
Jack J. Katzfey ◽  
Kim C. Nguyen ◽  
John L. McGregor
Keyword(s):  
New Zealand ◽  
Regional Model ◽  
Model Simulations

ON REGIONAL MODEL SIMULATIONS OF CLIMATE CHANGE OVER NEW ZEALAND

1999 ◽  
Vol 19 ◽  
pp. 3 ◽  
Author(s):  
Renwick ◽  
Katzfey ◽  
McGregor ◽  
Nguyen
Keyword(s):  
Climate Change ◽  
New Zealand ◽  
Regional Model ◽  
Model Simulations

scholarly journals New concepts for the comparison of tropospheric NO<sub>2</sub> column densities derived from car-MAX-DOAS observations, OMI satellite observations and the regional model CHIMERE during two MEGAPOLI campaigns in Paris 2009/10

2015 ◽  
Vol 8 (3) ◽  
pp. 2437-2500
Author(s):  
R. Shaiganfar ◽  
S. Beirle ◽  
H. Petetin ◽  
Q. Zhang ◽  
M. Beekmann ◽  
...  
Keyword(s):  
Power Plants ◽  
Transport Model ◽  
Spatial Scales ◽  
Regional Model ◽  
Satellite Observations ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Model Simulations ◽  
Comparison Results

Abstract. We compare tropospheric column densities (vertically integrated concentrations) of NO2 from three data sets for the metropolitan area of Paris during two extensive measurement campaigns (25 days in summer 2009 and 29 days in winter 2010) within the European research project MEGAPOLI. The selected data sets comprise a regional chemical transport model (CHIMERE) as well as two observational data sets: ground based mobile Multi-AXis-Differential Optical Absorption Spectroscopy (car-MAX-DOAS) measurements and satellite measurements from the Ozone Monitoring Instrument (OMI). On most days, car-MAX-DOAS measurements were carried out along large circles (diameter ~35 km) around Paris. The car-MAX-DOAS results are compared to coincident data from CHIMERE and OMI. All three data sets have their specific strengths and weaknesses, especially with respect to their spatio-temporal resolution and coverage as well as their uncertainties. Thus we compare them in two different ways: first, we simply consider the original data sets. Second, we compare modified versions making synergistic use of the complementary information from different data sets. For example, profile information from the regional model is used to improve the satellite data, observations of the horizontal trace gas distribution are used to adjust the respective spatial patterns of the model simulations, or the model is used as a transfer tool to bridge the spatial scales between car-MAX-DOAS and satellite observations. Using the modified versions of the data sets, the comparison results substantially improve compared to the original versions. In general, good agreement between the data sets is found outside the emission plume, but inside the emission plumes the tropospheric NO2 VCDs are systematically underestimated by the CHIMERE model and the satellite observations (compared to the car-MAX-DOAS observations). One major result from our study is that for satellite validation close to strong emission sources (like power plants or megacities) detailed information about the intra-pixel heterogeneity is essential. Such information may be gained from simultaneous car-MAX-DOAS measurements using multiple instruments or by combining (car-) MAX-DOAS measurements with results from regional model simulations.

scholarly journals The weather@home regional climate modelling project for Australia and New Zealand

2016 ◽  
Author(s):  
Mitchell T. Black ◽  
David J. Karoly ◽  
Suzanne M. Rosier ◽  
Sam M. Dean ◽  
Andrew D. King ◽  
...  
Keyword(s):  
New Zealand ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Model Performance ◽  
Atmospheric Model ◽  
Regional Model ◽  
Climate Simulation ◽  
Climate Modelling ◽  
Climate Scenarios ◽  
Home Computers

Abstract. A new climate modelling project has been developed for regional climate simulation and the attribution of weather and climate extremes over Australia and New Zealand. The project, known as weather@home Australia-New Zealand, uses public volunteers' home computers to run a moderate-resolution global atmospheric model with a nested regional model over the Australasian region. By harnessing the aggregated computing power of home computers, weather@home is able to generate an unprecedented number of simulations of possible weather under various climate scenarios. This combination of large ensemble sizes with high spatial resolution allows extreme events to be examined with more robust estimates of uncertainty. This paper provides an overview of the weather@home Australia-New Zealand project, including initial evaluation of the regional model performance. The model is seen to be capable of resolving many climate features that are important for the Australian and New Zealand regions, including the influence of El Niño-Southern Oscillation on driving natural climate variability. To date, 75 model simulations of the observed climate have been successfully integrated over the period 1985–2014 in a time-slice manner. In addition, multi-thousand member ensembles have also been generated for the years 2013, 2014 and 2015 under climate scenarios with and without the effect of human influences. All data generated by the project is freely available to the broader research community.

scholarly journals Improving Regional Model Simulations with Precipitation Assimilation

2005 ◽  
Vol 9 (20) ◽  
pp. 1-44 ◽  
Author(s):  
Ana M. B. Nunes ◽  
John O. Roads
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Initial Conditions ◽  
Regional Model ◽  
Seasonal Forecasts ◽  
Wet Season ◽  
Model Simulations ◽  
Precipitation Simulation ◽  
Global Reanalysis ◽  
Model Precipitation

Abstract Although large-scale atmospheric reanalyses are now providing physical, realistic fields for many variables, precipitation remains problematic. As shown in recent studies, using a regional model to downscale the global reanalysis only marginally alleviates the precipitation simulation problems. For this reason, later-generation analyses, including the recent National Centers for Environmental Prediction regional reanalysis, are using precipitation assimilation as a methodology to provide superior precipitation fields. This methodology can also be applied to regional model simulations to substantially improve the precipitation fields downscaled from global reanalysis. As an illustration of the regional model precipitation assimilation impact, the authors describe here an extended-range simulation comparison over South America. The numerical experiments cover the beginning of the Large-Scale Biosphere–Atmosphere wet season campaign of January 1999. Evaluations using radiosonde datasets obtained during this campaign are provided as well. As will be shown, rain-rate assimilation not only increases the regional model precipitation simulation skill but also provides improvements in other fields influenced by the precipitation. Because of the potential impact on land surface features, the authors believe they will ultimately be able to show improvements in monthly to seasonal forecasts when precipitation assimilation is used to generate more accurate land surface initial conditions.

scholarly journals Reliability of regional climate model simulations of extremes and of long-term climate

2004 ◽  
Vol 4 (3) ◽  
pp. 417-431 ◽  
Author(s):  
U. Böhm ◽  
M. Kücken ◽  
D. Hauffe ◽  
F.-W. Gerstengarbe ◽  
P. C. Werner ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Model ◽  
Northeastern Brazil ◽  
Near Surface ◽  
Surface Parameters ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present two case studies that demonstrate how a common evaluation methodology can be used to assess the reliability of regional climate model simulations from different fields of research. In Case I, we focused on the agricultural yield loss risk for maize in Northeastern Brazil during a drought linked to an El-Niño event. In Case II, the present-day regional climatic conditions in Europe for a 10-year period are simulated. To comprehensively evaluate the model results for both kinds of investigations, we developed a general methodology. On its basis, we elaborated and implemented modules to assess the quality of model results using both advanced visualization techniques and statistical algorithms. Besides univariate approaches for individual near-surface parameters, we used multivariate statistics to investigate multiple near-surface parameters of interest together. For the latter case, we defined generalized quality measures to quantify the model's accuracy. Furthermore, we elaborated a diagnosis tool applicable for atmospheric variables to assess the model's accuracy in representing the physical processes above the surface under various aspects. By means of this evaluation approach, it could be demonstrated in Case Study I that the accuracy of the applied regional climate model resides at the same level as that we found for another regional model and a global model. Excessive precipitation during the rainy season in coastal regions could be identified as a major contribution leading to this result. In Case Study II, we also identified the accuracy of the investigated mean characteristics for near-surface temperature and precipitation to be comparable to another regional model. In this case, an artificial modulation of the used initial and boundary data during preprocessing could be identified as the major source of error in the simulation. Altogether, the achieved results for the presented investigations indicate the potential of our methodology to be applied as a common test bed to different fields of research in regional climate modeling.

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