Resolution dependence in simulating the African hydroclimate with the HadGEM3-RA regional climate model

Moufouma-Okia, Wilfran; Jones, R. G.

doi:10.1007/s00382-014-2322-2

Cited by 29 publications

(25 citation statements)

References 84 publications

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“…Figure suggests that for this season, qflux from the east is in part influenced by model representation of topography. However, this relationship is complex or nonexistent for the rest of the year, which is perhaps expected as previous studies have found no simple relationship between rainfall and model resolution over this region [ Washington et al ., ; Moufouma‐Okia and Jones , ]. DJF stands out as having the strongest relationships with qflux across several boundaries, in particular the east and across the northeastern and southeastern corners of the domain.…”

Section: Discussionmentioning

confidence: 99%

Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

Creese

Washington

2016

JGR Atmospheres

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Coupled models are the tools by which we diagnose and project future climate, yet in certain regions they are critically underevaluated. The Congo Basin is one such region which has received limited scientific attention, due to the severe scarcity of observational data. There is a large difference in the climatology of rainfall in global coupled climate models over the basin. This study attempts to address this research gap by evaluating modeled rainfall magnitude and distribution amongst global coupled models in the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Mean monthly rainfall between models varies by up to a factor of 5 in some months, and models disagree on the location of maximum rainfall. The ensemble mean, which is usually considered a “best estimate” of coupled model output, does not agree with any single model, and as such is unlikely to present a possible rainfall state. Moisture flux (qflux) convergence (which is assumed to be better constrained than parameterized rainfall) is found to have a strong relationship with rainfall; strongest correlations occur at 700 hPa in March–May (r = 0.70) and 850 hPa in June–August, September–November, and December–February (r = 0.66, r = 0.71, and r = 0.81). In the absence of observations, this relationship could be used to constrain the wide spectrum of modeled rainfall and give a better understanding of Congo rainfall climatology. Analysis of moisture transport pathways indicates that modeled rainfall is sensitive to the amount of moisture entering the basin. A targeted observation campaign at key Congo Basin boundaries could therefore help to constrain model rainfall.

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Section: Discussionmentioning

confidence: 99%

Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

Creese

Washington

2016

JGR Atmospheres

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“…Wang et al , ) to ∼150 km (e.g. Moufouma‐Okia and Jones, ). The latitude–longitude grid is rotated so that the equator lies always inside the region of interest, in order to obtain a quasi‐uniform grid box area throughout the region of interest.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the added value of a high‐resolution regional climate model simulation of the South Asian summer monsoon climatology

Karmacharya

Jones

Moufouma-Okia

et al. 2016

Intl Journal of Climatology

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The South Asian summer monsoon (SASM) is a continental scale weather phenomenon, which fluctuates at a range of temporal and spatial scales. Although majority of global climate models are broadly able to simulate the large scale characteristics of the SASM, they generally have major deficiencies such as constraints in reproducing observed mean precipitation. It is generally anticipated that higher resolution regional climate models (RCMs) would be able to simulate an improved mean state owing to their capacity to better simulate fine temporal and spatial scale features and variability. Here, we analyse SASM simulations using a contemporary Hadley Centre RCM, forced by ERA‐Interim reanalysis and observed sea surface temperature, at medium (0.44°) and high (0.11°) horizontal resolutions. Evaluation of the results show that, compared to the medium resolution RCM, the high resolution RCM is able to better resolve the interaction of the low level monsoon flow with the Himalayan orography leading to added value in simulating many aspects of SASM precipitation such as the seasonal mean, relative frequency distribution of daily precipitation, and various metrics of precipitation extremes. In contrast to many previous studies, maximum added value is note along the Indo‐Gangetic plain rather than over the complex Himalayas, and the added values of up to 5 mm day−1 and 50 days are noted for mean precipitation and number of wet days, respectively over the region. Similarly, added values of up to 15 and 3 mm day−1 are noted for 95th percentile of daily precipitation and simple daily intensity index, respectively over central India and the Himalayan range. These results suggest that higher resolution RCMs have the potential to add more value when downscaling global climate model climate change projections.

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“…In MPI‐REMO, the first phase is strongly overestimated and the second phase is underestimated. This can be related to a misrepresentation of precipitation over the coastal region (Moufouma‐Okia & Jones, ; Vondou & Haensler, ). The spatial correlation values (

r

) between each data sets and the observations ensemble mean is also presented on Figure .…”

Section: Resultsmentioning

confidence: 99%

CORDEX Multi‐RCM Hindcast Over Central Africa: Evaluation Within Observational Uncertainty

et al. 2020

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This paper investigates the performance of 10 Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998–2008 and performed over a common model grid spacing 0.44° ( ∼50 km). Multiple observational data sets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC‐WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC‐WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not generally outperform individual RCMs realization as it is reported in several previous studies.

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Resolution dependence in simulating the African hydroclimate with the HadGEM3-RA regional climate model

Cited by 29 publications

References 84 publications

Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

Evaluation of the added value of a high‐resolution regional climate model simulation of the South Asian summer monsoon climatology

CORDEX Multi‐RCM Hindcast Over Central Africa: Evaluation Within Observational Uncertainty

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