scholarly journals Bayesian model averaging of the RegCM temperature projections: a Canadian case study

2021 ◽  
Author(s):  
Tangnyu Song ◽  
Guohe Huang ◽  
Guoqing Wang ◽  
Yongping Li ◽  
Xiuquan Wang ◽  
...  
Keyword(s):  
Bayesian Model ◽  
General Circulation ◽  
Bayesian Model Averaging ◽  
Climate Model ◽  
Regional Climate ◽  
Model Averaging ◽  
Circulation Model ◽  
Ensemble Member ◽  
Eastern Canada ◽  
Pbl Scheme

Abstract The choices of physical schemes coupled in the regional climate model (RegCM), the input general circulation model (GCM) results, and the emission scenarios may cause considerable uncertainties in future temperature projections. Therefore, the ensemble approach, which can be used to reflect these uncertainties, is highly desired. In this study, the probabilistic projections for future temperature are generated at 88 Canadian climate stations based on the developed RegCM ensemble and obtained Bayesian model averaging (BMA) weights. The BMA weights indicate that the RegCM coupled with the holtslag PBL scheme driven by the HadGEM can provide relatively reliable temperature projections at most climate stations. It is also suggested that the BMA approach is effective in simulating temperature over middle and eastern Canada through taking the advantage of each ensemble member. However, the effectiveness of the BMA method is limited when all the models in the ensemble cannot simulate the temperature robustly. The projected results demonstrate that the temperature will increase continuously in the future, while the temperature increase under RCP8.5 will be significantly larger than that under RCP4.5.

scholarly journals Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1543
Author(s):  
Reinhardt Pinzón ◽  
Noriko N. Ishizaki ◽  
Hidetaka Sasaki ◽  
Tosiyuki Nakaegawa
Keyword(s):  
Regional Climate Model ◽  
Air Temperature ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Circulation Model ◽  
Horizontal Resolution ◽  
Temperature And Precipitation ◽  
Current Climate

To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.

scholarly journals Applying an isotope-enabled regional climate model over the Greenland ice sheet: effect of spatial resolution on model bias

2021 ◽  
Vol 17 (4) ◽  
pp. 1685-1699
Author(s):  
Marcus Breil ◽  
Emanuel Christner ◽  
Alexandre Cauquoin ◽  
Martin Werner ◽  
Melanie Karremann ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Circulation Model ◽  
Ice Cores ◽  
Arctic Region ◽  
The Arctic ◽  
Simulation Results ◽  
The Arctic Region

Abstract. In order to investigate the impact of spatial resolution on the discrepancy between simulated δ18O and observed δ18O in Greenland ice cores, regional climate simulations are performed with the isotope-enabled regional climate model (RCM) COSMO_iso. For this purpose, isotope-enabled general circulation model (GCM) simulations with the ECHAM5-wiso general circulation model (GCM) under present-day conditions and the MPI-ESM-wiso GCM under mid-Holocene conditions are dynamically downscaled with COSMO_iso for the Arctic region. The capability of COSMO_iso to reproduce observed isotopic ratios in Greenland ice cores for these two periods is investigated by comparing the simulation results to measured δ18O ratios from snow pit samples, Global Network of Isotopes in Precipitation (GNIP) stations and ice cores. To our knowledge, this is the first time that a mid-Holocene isotope-enabled RCM simulation is performed for the Arctic region. Under present-day conditions, a dynamical downscaling of ECHAM5-wiso (1.1∘×1.1∘) with COSMO_iso to a spatial resolution of 50 km improves the agreement with the measured δ18O ratios for 14 of 19 observational data sets. A further increase in the spatial resolution to 7 km does not yield substantial improvements except for the coastal areas with its complex terrain. For the mid-Holocene, a fully coupled MPI-ESM-wiso time slice simulation is downscaled with COSMO_iso to a spatial resolution of 50 km. In the mid-Holocene, MPI-ESM-wiso already agrees well with observations in Greenland and a downscaling with COSMO_iso does not further improve the model–data agreement. Despite this lack of improvement in model biases, the study shows that in both periods, observed δ18O values at measurement sites constitute isotope ratios which are mainly within the subgrid-scale variability of the global ECHAM5-wiso and MPI-ESM-wiso simulation results. The correct δ18O ratios are consequently not resolved in the GCM simulation results and need to be extracted by a refinement with an RCM. In this context, the RCM simulations provide a spatial δ18O distribution by which the effects of local uncertainties can be taken into account in the comparison between point measurements and model outputs. Thus, an isotope-enabled GCM–RCM model chain with realistically implemented fractionating processes constitutes a useful supplement to reconstruct regional paleo-climate conditions during the mid-Holocene in Greenland. Such model chains might also be applied to reveal the full potential of GCMs in other regions and climate periods, in which large deviations relative to observed isotope ratios are simulated.

scholarly journals Sensitivity of Pliocene climate simulations in MRI-CGCM2.3 to respective boundary conditions

2016 ◽  
Vol 12 (8) ◽  
pp. 1619-1634 ◽  
Author(s):  
Youichi Kamae ◽  
Kohei Yoshida ◽  
Hiroaki Ueda
Keyword(s):  
Boundary Conditions ◽  
General Circulation ◽  
Model Comparison ◽  
Climate Model ◽  
Phase 1 ◽  
Regional Climate ◽  
Circulation Model ◽  
Poleward Heat Transport ◽  
Climate Simulations ◽  
Intercomparison Project

Abstract. Accumulations of global proxy data are essential steps for improving reliability of climate model simulations for the Pliocene warming climate. In the Pliocene Model Intercomparison Project phase 2 (PlioMIP2), a part project of the Paleoclimate Modelling Intercomparison Project phase 4, boundary forcing data have been updated from the PlioMIP phase 1 due to recent advances in understanding of oceanic, terrestrial and cryospheric aspects of the Pliocene palaeoenvironment. In this study, sensitivities of Pliocene climate simulations to the newly archived boundary conditions are evaluated by a set of simulations using an atmosphere–ocean coupled general circulation model, MRI-CGCM2.3. The simulated Pliocene climate is warmer than pre-industrial conditions for 2.4 °C in global mean, corresponding to 0.6 °C warmer than the PlioMIP1 simulation by the identical climate model. Revised orography, lakes, and shrunk ice sheets compared with the PlioMIP1 lead to local and remote influences including snow and sea ice albedo feedback, and poleward heat transport due to the atmosphere and ocean that result in additional warming over middle and high latitudes. The amplified higher-latitude warming is supported qualitatively by the proxy evidences, but is still underestimated quantitatively. Physical processes responsible for the global and regional climate changes should be further addressed in future studies under systematic intermodel and data–model comparison frameworks.

scholarly journals Estimation of discharge from Langtang River basin, Rasuwa, Nepal, using a glacio-hydrological model

2014 ◽  
Vol 55 (66) ◽  
pp. 223-230 ◽  
Author(s):  
Niraj S. Pradhananga ◽  
Rijan B. Kayastha ◽  
Bikas C. Bhattarai ◽  
Tirtha R. Adhikari ◽  
Suresh C. Pradhan ◽  
...  
Keyword(s):  
River Basin ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Circulation Model ◽  
Glacier Area ◽  
Monsoon Period ◽  
Ice Melt ◽  
The Future ◽  
Total Discharge

AbstractThis paper provides the results of semi-distributed positive degree-day (PDD) modelling for a glacierized river basin in Nepal. The main objective is to estimate the present and future discharge from the glacierized Langtang River basin using a PDD model (PDDM). The PDDM is calibrated for the period 1993–98 and is validated for the period 1999–2006 with Nash–Sutcliffe values of 0.85 and 0.80, respectively. Furthermore, the projected precipitation and temperature data from 2010 to 2050 are obtained from the Bjerknes Centre for Climate Research, Norway, for the representative concentration pathway 4.5 (RCP4.5) scenario. The Weather Research and Forecasting regional climate model is used to downscale the data from the Norwegian Earth System Model general circulation model. Projected discharge shows no significant trend, but in the future during the pre-monsoon period, discharge will be high and the peak discharge will be in July whereas it is in August at present. The contribution of snow and ice melt from glaciers and snowmelt from rocks and vegetation will decrease in the future: in 2040–50 it will be just 50% of the total discharge. The PDDM is sensitive to monthly average temperature, as a 2°C temperature increase will increase the discharge by 31.9%. Changes in glacier area are less sensitive, as glacier area decreases of 25% and 50% result in a change in the total discharge of –5.7% and –11.4%, respectively.

scholarly journals Addressing the Issue of Systematic Errors in a Regional Climate Model

2007 ◽  
Vol 20 (5) ◽  
pp. 801-818 ◽  
Author(s):  
Vasubandhu Misra
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Circulation Model ◽  
Systematic Errors ◽  
Driver Model ◽  
South American ◽  
Driving Model

Abstract A methodology is proposed in which a few prognostic variables of a regional climate model (RCM) are strongly constrained at certain wavelengths to what is prescribed from the bias-corrected atmospheric general circulation model (AGCM; driver model) integrations. The goal of this strategy is to reduce the systematic errors in a RCM that mainly arise from two sources: the lateral boundary conditions and the RCM errors. Bias correction (which essentially corrects the climatology) of the forcing from the driving model addresses the former source while constraining the solution of the RCM beyond certain relatively large wavelengths in the regional domain [also termed as scale-selective bias correction (SSBC)] addresses the latter source of systematic errors in RCM. This methodology is applied to experiments over the South American monsoon region. It is found that the combination of bias correction and SSBC on the nested variables of divergence, vorticity, and the log of surface pressure of an RCM yields a major improvement in the simulation of the regional climate variability over South America from interannual to intraseasonal time scales. The basis for such a strategy is derived from a systematic empirical approach that involved over 100 regional seasonal climate integrations.

scholarly journals Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

2007 ◽  
Vol 135 (9) ◽  
pp. 3209-3220 ◽  
Author(s):  
J. Mc Lean Sloughter ◽  
Adrian E. Raftery ◽  
Tilmann Gneiting ◽  
Chris Fraley
Keyword(s):  
Pacific Northwest ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Weighted Average ◽  
Ensemble Member ◽  
Original Form ◽  
Cube Root ◽  
Precipitation Forecasting ◽  
The North

Abstract Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts’ relative contributions to predictive skill over a training period. It was developed initially for quantities whose PDFs can be approximated by normal distributions, such as temperature and sea level pressure. BMA does not apply in its original form to precipitation, because the predictive PDF of precipitation is nonnormal in two major ways: it has a positive probability of being equal to zero, and it is skewed. In this study BMA is extended to probabilistic quantitative precipitation forecasting. The predictive PDF corresponding to one ensemble member is a mixture of a discrete component at zero and a gamma distribution. Unlike methods that predict the probability of exceeding a threshold, BMA gives a full probability distribution for future precipitation. The method was applied to daily 48-h forecasts of 24-h accumulated precipitation in the North American Pacific Northwest in 2003–04 using the University of Washington mesoscale ensemble. It yielded predictive distributions that were calibrated and sharp. It also gave probability of precipitation forecasts that were much better calibrated than those based on consensus voting of the ensemble members. It gave better estimates of the probability of high-precipitation events than logistic regression on the cube root of the ensemble mean.

scholarly journals Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

2020 ◽  
Vol 21 (10) ◽  
pp. 2401-2418 ◽  
Author(s):  
E. C. Massoud ◽  
H. Lee ◽  
P. B. Gibson ◽  
P. Loikith ◽  
D. E. Waliser
Keyword(s):  
United States ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Climate Model ◽  
Model Averaging ◽  
Daily Rainfall ◽  
Climate Projections ◽  
Total Uncertainty ◽  
Model Average ◽  
Ensemble Mean

AbstractThis study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

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