The impact of lateral boundary data errors on the simulated climate of a nested regional climate model

2006 ◽  
Vol 28 (4) ◽  
pp. 333-350 ◽  
Author(s):  
Emilia Paula Diaconescu ◽  
René Laprise ◽  
Laxmi Sushama
Keyword(s):  
Regional Climate Model ◽  
Boundary Data ◽  
Climate Model ◽  
Regional Climate ◽  
Lateral Boundary ◽  
Data Errors ◽  
Simulated Climate ◽  
The Impact

scholarly journals The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

2018 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

scholarly journals Estimating the Uncertainty in a Regional Climate Model Related to Initial and Lateral Boundary Conditions

2005 ◽  
Vol 18 (7) ◽  
pp. 917-933 ◽  
Author(s):  
Wanli Wu ◽  
Amanda H. Lynch ◽  
Aaron Rivers
Keyword(s):  
Boundary Conditions ◽  
Climate Model ◽  
Initial Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Modeling ◽  
Regional Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
The Impact

Abstract There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected.

Projected climate change over Kuwait simulated using a WRF high resolution regional climate model

2020 ◽  
Author(s):  
Hussain Alsarraf
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Dynamic Downscaling ◽  
Model Simulations ◽  
Average Maximum ◽  
The Impact

<p>The purpose of this study is to examine the impact of climate change on the changes on summer surface temperatures between present (2000-2010) and future (2050-2060) over the Arabian Peninsula and Kuwait. In this study, the influence of climate change in the Arabian Peninsula and especially in Kuwait was investigated by high resolution (36, 12, and 4 km grid spacing) dynamic downscaling from the Community Climate System Model CCSM4 using the WRF Weather Research and Forecasting model. The downscaling results were first validated by comparing National Centers for Environmental Prediction NCEP model outputs with the observational data. The global climate change dynamic downscaling model was run using WRF regional climate model simulations (2000-2010) and future projections (2050-2060). The influence of climate change in the Arabian Peninsula can be projected from the differences between the two period’s model simulations. The regional model simulations of the average maximum surface temperature in summertime predicted an increase from 1◦C to 3 ◦C over the summertime in Kuwait by midcentury.</p><p><strong> </strong></p>

scholarly journals Regional Climate Model Simulations of the 1998 Summer China Flood: Dependence on Initial and Lateral Boundary Conditions

2011 ◽  
Vol 5 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Shuyan Liu ◽  
Xin-Zhong Liang ◽  
Wei Gao ◽  
Yuxiang He ◽  
Tiejun Ling
Keyword(s):  
Boundary Conditions ◽  
Regional Climate Model ◽  
Yangtze River ◽  
Climate Model ◽  
Yangtze River Basin ◽  
Regional Climate ◽  
Lateral Boundary ◽  
The Yangtze River ◽  
Lateral Boundary Conditions ◽  
The Yangtze River Basin

The dependence of the RegCM3 (Regional Climate Model version 3) downscaling skill on initial conditions (ICs) and lateral boundary conditions (LBCs) are investigated for the 1998 summer flood along the Yangtze River Basin in China. The effect of IC uncertainties is depicted by 15 realizations starting on each consecutive day from April 1 to 15 while all ending on September 1, 1998 with identical driving LBCs, analyses are based on June, July and August simulations. The result reveals certain IC effect on precipitation for daily evolution but little for summer mean geographical distribution. In contrast, the effect of LBCs uncertainties as represented by four different reanalyses are notably larger in both daily evolution and summer mean distribution. The ensemble average among either 15 IC realizations or 4 LBC runs does not show important skill improvement over the individuals. None of the RegCM3 simulations (including the ensemble means) captured the observed main rain band along the Yangtze River Basin. This general failure suggests the need for further model physics improvement.

scholarly journals Investigation of the Sensitivity of Water Cycle Components Simulated by the Canadian Regional Climate Model to the Land Surface Parameterization, the Lateral Boundary Data, and the Internal Variability

2009 ◽  
Vol 10 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Biljana Music ◽  
Daniel Caya
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Water Budget ◽  
Climate Model ◽  
Initial Conditions ◽  
Regional Climate ◽  
Internal Variability ◽  
Lateral Boundary ◽  
Annual Cycles ◽  
Lateral Boundary Conditions

Abstract This study investigates the sensitivity of components of the hydrological cycle simulated by the Canadian Regional Climate Model (CRCM) to lateral boundary forcing, the complexity of the land surface scheme (LSS), and the internal variability arising from different models’ initial conditions. This evaluation is a contribution to the estimation of the uncertainty associated to regional climate model (RCM) simulations. The analysis was carried out over the period 1961–99 for three North American watersheds, and it looked at climatological seasonal means, mean (climatological) annual cycles, and interanual variability. The three watersheds—the Mississippi, the St. Lawrence, and the Mackenzie River basins—were selected to cover a large range of climate conditions. An evaluation of simulated water budget components with available observations was also included in the analysis. Results indicated that the response of climatological means and annual cycles of water budget components to land surface parameterizations and lateral boundary conditions varied from basin to basin. Sensitivity to lateral boundary conditions is, in general, smaller than sensitivity to LSS and tends to be stronger for the northern basins (Mackenzie and St. Lawrence). Interannual variability was unaffected by changes in LSS and in driving data. Internal variability triggered by different initial conditions and the nonlinear nature of the climate model did not significantly affect either the 39-yr climatology, the climatological annual cycles, or the interannual variability. A comparison with observations suggests that although the simple Manabe-based LSS may be adequate for simulations of climatological means, skillful simulation of annual cycles require the use of a state-of-the-art LSS.

scholarly journals The Impact of Soil Moisture on Precipitation in a Regional Climate Model.

1998 ◽  
Vol 11 (5) ◽  
pp. 482-491
Author(s):  
Shinjiro KANAE ◽  
Taikan OKI ◽  
Katumi MUSIAKE
Keyword(s):  
Soil Moisture ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
The Impact

scholarly journals Can Bias Correction of Regional Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall Variability?

2017 ◽  
Vol 30 (24) ◽  
pp. 9785-9806 ◽  
Author(s):  
Eytan Rocheta ◽  
Jason P. Evans ◽  
Ashish Sharma
Keyword(s):  
Boundary Conditions ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Low Frequency ◽  
Lateral Boundary ◽  
Model Simulations ◽  
Low Frequency Variability ◽  
Climate Model Simulations

Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.

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