Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

Dieng, Diarra; Cannon, Alex J.; Laux, Patrick; Hald, Cornelius; Adeyeri, Oluwafemi E.; Rahimi, Jaber; Srivastava, Amit Kumar; Mbaye, Mamadou; Kunstmann, Harald

doi:10.1029/2021jd034836

Cited by 40 publications

(27 citation statements)

References 185 publications

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“…Notably, all models behaved differently in capturing the ECDF of different indices and for different continents. These inconsistencies could be attributed to the various land surface schemes and simulation of features such as vegetation 36 and orography 47 , unrealistic large-scale variability 35 , 48 , and contrasting internal variability between climate models and observations 23 , 49 .…”

Section: Discussionmentioning

confidence: 99%

“…These performance metrics were combined for a universal rank Eqs. ( 1 ) and ( 2 ) in which all metrics were assigned an equal weight 35 , 36 . where is the error of metric j and particular model i in space and time.…”

Section: Methodsmentioning

confidence: 99%

“…The performance of each GCM was ranked for each climate index during the historical period using five different metrics 23,[33][34][35] ; namely, percentage bias, mean absolute error, index of agreement, correlation, and root mean square error. These performance metrics were combined for a universal rank Eqs.…”

Section: Gcm Rankingmentioning

confidence: 99%

“…These performance metrics were combined for a universal rank Eqs. ( 1) and (2) in which all metrics were assigned an equal weight 35,36 . The number of dry periods of n days and more, during which the accumulated or maximal daily precipitation amount on a window of n days is under 1.0 mm (days) Dry Spell Frequency HW, AF DSTL Total number of days in dry periods of a minimum length, during which the maximum or accumulated precipitation within a window of the same length is under 1.0 mm (days) Dry Spell Total Length HW, AF ETR…”

Section: Gcm Rankingmentioning

confidence: 99%

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The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Gcm Rankingmentioning

confidence: 99%

Section: Gcm Rankingmentioning

confidence: 99%

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The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

Self Cite

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“…They cannot adjust the inter-variable dependencies, which are important for representing physical processes and estimating compound hazards. It was not until quite recently that the multivariate BC technique was considered and proposed (e.g., Bárdossy & Pegram, 2012;Cannon, 2018;Mehrotra & Sharma, 2015, 2016Robin et al, 2019;Vrac, 2018), and they have been applied to various climate change impact studies (Dieng et al, 2022;Meyer et al, 2019;Qiu et al, 2022;Zscheischler et al, 2019). Although it is intuitively recognized that multivariate BC could be more suitable for dealing with climate variables characterized by a strong physical linkage in nature, an unambiguous assessment of univariate and multivariate BC methods is essential to understand the potential limitations of individual methods and to avoid misleading application.…”

mentioning

confidence: 99%

Direct and Indirect Application of Univariate and Multivariate Bias Corrections on Heat-stress Indices based on Multi-RCM Simulations

Qiu

Min

et al. 2022

Preprint

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Abstract. Statistical bias correction (BC) is a widely used tool to post-process climate model biases for heat-stress impact studies, which are often based on indices calculated from multiple dependent variables. This study compares five bias correction methods (four univariate and one multivariate) with two applying strategies (direct and indirect) for correcting two heat-stress indices with different dependencies on temperature and relative humidity, using multiple Regional Climate Model simulations over South Korea. It would be helpful for reducing the ambiguity involved in the practical application of BC for climate modeling as well as end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence and therefore benefit the indirect correction of heat-stress indices that depend on the adjustment of individual components, especially those relying equally on multiple drivers. On the other hand, the direct correction of multivariate indices using the Quantile Delta Mapping univariate approach can also produce a comparable performance in the corrected heat-stress indices. However, our results also indicate that attention should be paid to the non-stationarity of bias brought by climate sensitivity in the modeled data, which may affect the bias-corrected results unsystematically. Careful interpretation of the correction process is required for an accurate heat-stress impact assessment.

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Assessment of NA-CORDEX regional climate models, reanalysis, and in-situ gridded-observational against U.S. Climate Reference Network datasets

Madonna

Serva

et al. 2022

Preprint

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Climate models still need to be improved in their capability of reproducing the present climate at both global and regional scale. The assessment of their performance depends on the datasets used as comparators. Reanalysis and gridded (homogenized or not homogenized) observational datasets have been frequently used for this purpose. However, none of these can be considered a reference dataset. Here, for the first time, using in-situ measurements from NOAA U.S. Climate Reference Network (USCRN), a network of 139 stations with high-quality instruments deployed across the continental U.S, daily temperature, and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX are assessed. The assessment is extended also to the most recent and modern widely used reanalysis (ERA5, ERA-Interim, MERRA2, NARR) and gridded observational datasets (Daymet, PRISM, Livneh, CPC). Results show that biases for the different datasets are mainly seasonal and subregional dependent. On average, reanalysis and in-situ-based datasets are generally warmer than USCRN year-round, while models are colder (warmer) in winter (summer). In-situ-based datasets provide the best performance in most of the CONUS regions compared to reanalysis and models, but still have biases in regions such as the Midwest mountains and the Northwestern Pacific. Results also highlight that reanalysis does not outperform RCMs in most of the U.S. subregions. Likewise, for both reanalysis and models, temperature and precipitation biases are also significantly depending on the orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.

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Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

Abstract: Future climate change projections over Africa show the likelihood of increased extreme weather conditions such as very hot days (when the maximum temperature exceeds 35°C), heat waves, and high fire-danger days (Engel-

Cited by 40 publications

References 185 publications

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Direct and Indirect Application of Univariate and Multivariate Bias Corrections on Heat-stress Indices based on Multi-RCM Simulations

Assessment of NA-CORDEX regional climate models, reanalysis, and in-situ gridded-observational against U.S. Climate Reference Network datasets

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