An Assessment of GCM Performance at a Regional Scale Using a Score-Based Method

A multicriteria score-based method was developed to assess the performances of 18 general circulation models (GCMs) in the study region from 1970 to 2005. The results indicate the following. (1) GCMs simulate temperature better than rainfall. The temporal and spatial distributions of simulated temperature performed well compared with those from the observations. In comparison to temperature, the spatial distribution of simulated precipitation performed poorly. Most of the GCMs underestimated temperature and overestimated precipitation. (2) The Grubbs test was used to detect anomalous moving changes in the rank score (RS) results; the inm-cm4 and ipsl-cm5b-lr models were rejected when simulating temperature, while the bnu-esm and canesm2 models performed poorly when simulating precipitation. (3) Adding or removing any criterion does not significantly influence the RS result, which indicates that the multicriteria score-based method is robust. The advantages of using multicriteria score-based method to assess GCMs performance were demonstrated, and this method also provides a more comprehensive assessment when compared with the single-criterion method. The multicriteria method could replace other criteria as the research requirements and could be easily extended to different study regions; the results could be used for better informed regional climate change impact analyses.

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Surface Energy Balances of Three General Circulation Models: Implications for Simulating Regional Climate Change

Journal of Climate ◽

10.1175/1520-0442(1991)004<0121:sebotg>2.0.co;2 ◽

1991 ◽

Vol 4 (2) ◽

pp. 121-134 ◽

Cited By ~ 25

Author(s):

William J. Gutowski ◽

David S. Gutzler ◽

Wei-Chyung Wang

Keyword(s):

Climate Change ◽

Surface Energy ◽

General Circulation ◽

Regional Climate ◽

Regional Climate Change ◽

General Circulation Models ◽

Energy Balances

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Extreme Precipitation Spatial Analog: In Search of an Alternative Approach for Future Extreme Precipitation in Urban Hydrological Studies

Water ◽

10.3390/w11051032 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1032 ◽

Cited By ~ 2

Author(s):

Ariel Wang ◽

Francina Dominguez ◽

Arthur Schmidt

Keyword(s):

Extreme Precipitation ◽

General Circulation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Change ◽

Regional Climate Models ◽

General Circulation Models ◽

Precipitation Frequency ◽

Spatio Temporal ◽

Climate Change Assessment

In this paper, extreme precipitation spatial analog is examined as an alternative method to adapt extreme precipitation projections for use in urban hydrological studies. The idea for this method is that real climate records from some cities can serve as “analogs” that behave like potential future precipitation for other locations at small spatio-temporal scales. Extreme precipitation frequency quantiles of a 3.16 km 2 catchment in the Chicago area, computed using simulations from North American Regional Climate Change Assessment Program (NARCCAP) Regional Climate Models (RCMs) with L-moment method, were compared to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 (NA14) quantiles at other cities. Variances in raw NARCCAP historical quantiles from different combinations of RCMs, General Circulation Models (GCMs), and remapping methods are much larger than those in NA14. The performance for NARCCAP quantiles tend to depend more on the RCMs than the GCMs, especially at durations less than 24-h. The uncertainties in bias-corrected future quantiles of NARCCAP are still large compared to those of NA14, and increase with rainfall duration. Results show that future 3-h and 30-day rainfall in Chicago will be similar to historical rainfall from Memphis, TN and Springfield, IL, respectively. This indicates that the spatial analog is potentially useful, but highlights the fact that the analogs may depend on the duration of the rainfall of interest.

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Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-17-0127.1 ◽

2018 ◽

Vol 99 (10) ◽

pp. 2093-2106 ◽

Cited By ~ 6

Author(s):

Ambarish V. Karmalkar

Keyword(s):

Climate Change ◽

North America ◽

General Circulation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Change ◽

Internal Variability ◽

General Circulation Models ◽

Eastern United States ◽

The Impact

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

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Assessment of the performance of CMIP5 and CORDEX-SA models over the drought-prone Bundelkhand region, India

Journal of Water and Climate Change ◽

10.2166/wcc.2020.177 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 133-144 ◽

Cited By ~ 1

Author(s):

Ankur Vishwakarma ◽

Mahendra Kumar Choudhary ◽

Mrityunjay Singh Chauhan

Keyword(s):

General Circulation ◽

Climate Models ◽

Regional Climate ◽

Coupled Model ◽

Central India ◽

General Circulation Models ◽

Coefficient Of Determination ◽

Geophysical Fluid Dynamics Laboratory ◽

Study Region ◽

Daily Precipitation Data

Abstract The present study evaluates the reliability of the latest generation five best general circulation models (GCMs) under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their corresponding regional climate models (RCMs) of Coordinated Regional Climate Downscaling Experiment (CORDEX) for the Bundelkhand region in central India. The study is performed on a microscale due to frequent drought events and more climate susceptibility in the study region. Observed daily precipitation data of 35 years (1971–2005) from the Indian Meteorological Department (IMD) have been chosen to check the performance of the models. Bilinear interpolation has been adopted to prepare all the data to obtain them on a common grid platform at a half-degree (0.5° × 0.5°) resolution. The data of the models have been bias-corrected using quantile mapping. Uncertainty of the models has been assessed using Nash–Sutcliffe efficiency (NSE), coefficient of determination (r2) and a modified method known as skill score (SS). The study concluded that the bias-corrected GCMs played a better role than the CORDEX RCMs for the Bundelkhand region. Earth System Model, ESM-2M of the Geophysical Fluid Dynamics Laboratory (GFDL) has shown better accuracy than all the CORDEX RCMs and their driving GCMs for the study region.

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An Improved Daily Weather Generator for the Assessment of Regional Climate Change Impacts

10.21203/rs.3.rs-320088/v1 ◽

2021 ◽

Author(s):

Mohammad Reza Khazaei ◽

Mehraveh Hasirchian ◽

Bagher Zahabiyoun

Keyword(s):

Climate Change ◽

General Circulation ◽

Regional Climate ◽

Low Frequency ◽

Climate Change Impacts ◽

Regional Climate Change ◽

General Circulation Models ◽

Weather Generator ◽

Weather Data ◽

Monthly Precipitation

Abstract Weather Generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the Improved Weather Generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulations run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of General Circulation Models (GCMs) scenarios to assess regional climate change impacts, especially hydrological effects.

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Simulating present and future climates of southern Africa using general circulation models

Progress in Physical Geography Earth and Environment ◽

10.1177/030913339702100104 ◽

1997 ◽

Vol 21 (1) ◽

pp. 51-78 ◽

Cited By ~ 57

Author(s):

A.M. Joubert ◽

B.C. Hewitson

Keyword(s):

Climate Change ◽

Southern Africa ◽

General Circulation ◽

Regional Climate ◽

Daily Rainfall ◽

Regional Climate Change ◽

Summer Rainfall ◽

General Circulation Models ◽

Empirical Downscaling ◽

Projected Changes

The current state of regional climate and climate change modelling using GCMs is reviewed for southern Africa, and several approaches to regional climate change prediction which have been applied to southern Africa are assessed. Confidence in projected regional changes is based on the ability of a range of models to simulate present regional climate, and is greatest where intermodel consensus in terms of the nature of projected changes is highest. Both equil ibrium and transient climate change projections for southern Africa are considered. Warming projected over southern Africa is within the range of globally averaged estimates. Uncertainties associated with the parameterization of convection ensure that projected changes in rainfall at GCM grid scales remain unreliable. However, empirical downscaling approaches produce rainfall changes consistent with synoptic-scale circulation. Both downscaling and grid-scale approaches indicate a 10-15% decrease in summer rainfall over the central interior which may have important implications for surface hydrology. Climate change may be manifested as a change in variability, and not in mean climate. Over southern Africa, increases in the variability and intensity of daily rainfall events are indicated.

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Two Approaches to Quantifying Uncertainty in Global Temperature Changes

Journal of Climate ◽

10.1175/jcli3895.1 ◽

2006 ◽

Vol 19 (19) ◽

pp. 4785-4796 ◽

Cited By ~ 52

Author(s):

Ana Lopez ◽

Claudia Tebaldi ◽

Mark New ◽

Dave Stainforth ◽

Myles Allen ◽

...

Keyword(s):

General Circulation ◽

Regional Climate ◽

Regional Climate Change ◽

General Circulation Models ◽

Temperature Changes ◽

Global Mean Temperature ◽

Statistical Approaches ◽

Quantifying Uncertainty ◽

Global Mean ◽

Probabilistic Projections

Abstract A Bayesian statistical model developed to produce probabilistic projections of regional climate change using observations and ensembles of general circulation models (GCMs) is applied to evaluate the probability distribution of global mean temperature change under different forcing scenarios. The results are compared to probabilistic projections obtained using optimal fingerprinting techniques that constrain GCM projections by observations. It is found that, due to the different assumptions underlying these statistical approaches, the predicted distributions differ significantly in particular in their uncertainty ranges. Results presented herein demonstrate that probabilistic projections of future climate are strongly dependent on the assumptions of the underlying methodologies.

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