On the development of a coupled regional climate–vegetation model RCM–CLM–CN–DV and its validation in Tropical Africa

et al. 2016

J Adv Model Earth Syst

Self Cite

In this study, we take an ensemble modeling approach using the regional climate model RegCM4.3.4‐CLM‐CN‐DV (RCM) to study the impact of including vegetation dynamics on model performance in simulating present‐day climate and on future climate projections over West Africa. The ensemble consists of four global climate models (GCMs) as lateral boundary conditions for the RCM, and simulations with both static and dynamic vegetation are conducted. The results demonstrate substantial sensitivity of the simulated precipitation, evapotranspiration, and soil moisture to vegetation representation. Although including dynamic vegetation in the model eliminates potential inconsistencies between surface climate and the bioclimatic requirements of the prescribed vegetation, it enhances model biases causing climate drift. For present‐day climate, the ensemble average generally outperforms individual members due to cancelation of model biases. For future changes, although the original GCMs project contradicting future rainfall trends over West Africa, the RCMs‐produced trends are generally consistent. The multimodel ensemble projects significant decreases of rainfall over a major portion of West Africa and significant increases over eastern Sahel and East Africa. Projected future changes of evapotranspiration and soil moisture are consistent with those of precipitation, with significant decreases (increases) for western (eastern) Sahel. Accounting for vegetation‐climate interactions has localized but significant impacts on projected future changes of precipitation, with a wet signal over a belt of projected increase of woody vegetation cover; the impact on the projected future changes of evapotranspiration and soil moisture over west and central Africa is much more profound.

Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multimodel ensemble simulations of present and future climates over West Africa: Impacts of vegetation dynamics

Erfanian

et al. 2016

J Adv Model Earth Syst

Self Cite

Comparison of RCM and GCM projections of boreal summer precipitation over Africa

Saini

et al. 2015

JGR Atmospheres

Self Cite

To provide input data for the potential future climate change impact assessment at regional levels, dynamic downscaling of global climate models (GCMs) climate using regional climate models (RCMs) is frequently utilized. It is important to understand how the climate change signal is modified in the RCM driven by GCM so that the simulated results can be accurately interpreted. This study compares the performance of a RCM and four driving GCMs in simulating precipitation over Africa and investigate how dynamically downscaled present and future climate from the regional model RegCM4.1 driven with four GCMs differ from those of the GCMs. In general, RegCM4.1 has a lower summer (June-August) precipitation bias than the driving GCMs, increasing our confidence in their future projections. Despite uncertainty in future projections, RegCM4.1 shows decreased precipitation over Sahel regardless of which GCM is used to drive the RCM. RegCM4.1 shows an increased interannual variability of summer precipitation over Gulf of Guinea and Central Africa as compared to GCMs in both present-day climate and projected future climate scenarios. The mean annual cycle of precipitation differs between RCM and the driving GCM, in particular during the West African Monsoon (WAM). The WAM onset, peak, and retreat phases are more clearly defined in the RegCM4.1 than the four driving GCMs, where the WAM rainband shifts more southward. This paper highlights the uncertainties in dynamical downscaling for climate prediction, which are due to the inconsistencies in the physical packages between RCM and GCMs.

Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets

Chen

2016

J Adv Model Earth Syst

Self Cite

Assessing and quantifying the uncertainties in projected future changes of energy and water budgets over land surface are important steps toward improving our confidence in climate change projections. In this study, the contribution of land surface models to the inter-GCM variation of projected future changes in land surface energy and water fluxes are assessed based on output from 19 global climate models (GCMs) and offline Community Land Model version 4 (CLM4) simulations driven by meteorological forcing from the 19 GCMs. Similar offline simulations using CLM4 with its dynamic vegetation submodel are also conducted to investigate how dynamic vegetation feedback, a process that is being added to more earth system models, may amplify or moderate the intermodel variations of projected future changes. Projected changes are quantified as the difference between the 2081-2100 period from the Representative Concentration Pathway 8.5 (RCP8.5) future experiment and the 1981-2000 period from the historical simulation. Under RCP8.5, projected changes in surface water and heat fluxes show a high degree of model dependency across the globe. Although precipitation is very likely to increase in the high latitudes of the Northern Hemisphere, a high degree of model-related uncertainty exists for evapotranspiration, soil water content, and surface runoff, suggesting discrepancy among land surface models (LSMs) in simulating the surface hydrological processes and snow-related processes. Large model-related uncertainties for the surface water budget also exist in the Tropics including southeastern South America and Central Africa. These uncertainties would be reduced in the hypothetical scenario of a single near-perfect land surface model being used across all GCMs, suggesting the potential to reduce uncertainties through the use of more consistent approaches toward land surface model development. Under such a scenario, the most significant reduction is likely to be seen in the Northern Hemisphere high latitudes. Including representation of vegetation dynamics is expected to further amplify the model-related uncertainties in projected future changes in surface water and heat fluxes as well as soil moisture content. This is especially the case in the high latitudes of the Northern Hemisphere (e.g., northwestern North America and central North Asia) where the projected vegetation changes are uncertain and in the Tropics (e.g., the Amazon and Congo Basins) where dense vegetation exists. Findings from this study highlight the importance of improving land surface model parameterizations related to soil and snow processes, as well as the importance of improving the accuracy of dynamic vegetation models.