Evaluation of CMIP5 Models in the Context of Dynamical Downscaling over Europe

2015 ◽  
Vol 28 (14) ◽  
pp. 5575-5582 ◽  
Author(s):  
Martin W. Jury ◽  
Andreas F. Prein ◽  
Heimo Truhetz ◽  
Andreas Gobiet

Abstract The quality of regional climate model (RCM) simulations is strongly dependent on the quality of data provided as lateral boundary conditions (LBCs). Frequently, the quality of near-surface variables of general circulation model (GCM) simulations like those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is analyzed in the region of interest. However, such analysis does not necessarily lead to the selection of high-quality LBCs, as demonstrated in this study. The study region is the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX), where a model performance index (MPI) is used to evaluate the skill of CMIP5 GCMs to reproduce near-surface variables within the EURO-CORDEX domain and free atmosphere variables along its lateral boundaries as a proxy for LBCs used in regional climate modeling. The results suggest that a GCM’s skill in simulating near-surface variables is correlated with 0.62 (Spearman’s r) to its skill in simulating LBCs for regional climate simulations. However, there is hardly any correlation between the performances of different variables, implying that a GCM evaluation solely based on surface parameters or a few variables is inadequate to select suitable driving data for regional climate models. The selection should include the evaluation of all variables passed to the RCM as LBCs in the lateral boundary zone (LBZ) on at least one midtropospheric level.

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1543
Author(s):  
Reinhardt Pinzón ◽  
Noriko N. Ishizaki ◽  
Hidetaka Sasaki ◽  
Tosiyuki Nakaegawa

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.


Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 675 ◽  
Author(s):  
Almazroui

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.


2021 ◽  
Vol 17 (4) ◽  
pp. 1685-1699
Author(s):  
Marcus Breil ◽  
Emanuel Christner ◽  
Alexandre Cauquoin ◽  
Martin Werner ◽  
Melanie Karremann ◽  
...  

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.


2016 ◽  
Vol 12 (8) ◽  
pp. 1619-1634 ◽  
Author(s):  
Youichi Kamae ◽  
Kohei Yoshida ◽  
Hiroaki Ueda

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.


2014 ◽  
Vol 55 (66) ◽  
pp. 223-230 ◽  
Author(s):  
Niraj S. Pradhananga ◽  
Rijan B. Kayastha ◽  
Bikas C. Bhattarai ◽  
Tirtha R. Adhikari ◽  
Suresh C. Pradhan ◽  
...  

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.


2007 ◽  
Vol 20 (5) ◽  
pp. 801-818 ◽  
Author(s):  
Vasubandhu Misra

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.


2008 ◽  
Vol 21 (12) ◽  
pp. 2835-2851 ◽  
Author(s):  
Andréa S. Taschetto ◽  
Ilana Wainer

Abstract This work investigates the reproducibility of precipitation simulated with an atmospheric general circulation model (AGCM) forced by subtropical South Atlantic sea surface temperature (SST) anomalies. This represents an important test of the model prior to investigating the impact of SSTs on regional climate. A five-member ensemble run was performed using the National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3). The CCM3 was forced by observed monthly SST over the South Atlantic from 20° to 60°S. The SST dataset used is from the Hadley Centre covering the period of September 1949–October 2001; this covers more than 50 yr of simulation. A statistical technique is used to determine the reproducibility in the CCM3 runs and to assess potential predictability in precipitation. Empirical orthogonal function analysis is used to reconstruct the ensemble using the most reproducible forced modes in order to separate the atmospheric response to local SST forcing from its internal variability. Results for reproducibility show a seasonal dependence, with higher values during austral autumn and spring. The spatial distribution of reproducibility shows that the tropical atmosphere is dominated by the underlying SSTs while variations in the subtropical–extratropical regions are primarily driven by internal variability. As such, changes in the South Atlantic convergence zone (SACZ) region are mainly dominated by internal atmospheric variability while the ITCZ has greater external dependence, making it more predictable. The reproducibility distribution reveals increased values after the reconstruction of the ensemble.


Sign in / Sign up

Export Citation Format

Share Document