scholarly journals A Time-Scale Decomposition Approach to Statistically Downscale Summer Rainfall over North China

2012 ◽  
Vol 25 (2) ◽  
pp. 572-591 ◽  
Author(s):  
Yan Guo ◽  
Jianping Li ◽  
Yun Li
Keyword(s):  
Time Scale ◽  
North China ◽  
Climate Models ◽  
Rainfall Variability ◽  
Summer Rainfall ◽  
Present Climate ◽  
Decomposition Approach ◽  
Southwest Indian Ocean ◽  
Interannual Rainfall Variability ◽  
Time Scale Decomposition

Abstract A time-scale decomposition (TSD) approach to statistically downscale summer rainfall over North China is described. It makes use of two distinct downscaling models respectively corresponding to the interannual and interdecadal rainfall variability. The two models were developed based on objective downscaling scheme that 1) identifies potential predictors based on correlation analysis between rainfall and considered climatic variables over the global scale and 2) selects the “optimal” predictors from the identified potential predictors via cross-validation-based stepwise regression. The downscaling model for the interannual rainfall variability is linked to El Niño–Southern Oscillation and the 850-hPa meridional wind over East China, while the one for the interdecadal rainfall variability is related to the sea level pressure over the southwest Indian Ocean. Taking the downscaled interannual and interdecadal components together the downscaled total rainfall was obtained. The results show that the TSD approach achieved a good skill to predict the observed rainfall with the correlation coefficient of 0.82 in the independent validation period. The authors further apply the model to obtain downscaled rainfall projections from three climate models under present climate and the A1B emission scenario in future. The resulting downscaled values provide a closer representation of the observation than the raw climate model simulations in the present climate; for the near future, climate models simulated a slight decrease in rainfall, while the downscaled values tend to be slightly higher than the present state.

scholarly journals The Role of Mesoscale Convective Complexes in Southern Africa Summer Rainfall

2013 ◽  
Vol 26 (5) ◽  
pp. 1654-1668 ◽  
Author(s):  
R. C. Blamey ◽  
C. J. C. Reason
Keyword(s):  
South Africa ◽  
Southern Africa ◽  
Rainfall Variability ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Summer Rainfall ◽  
Spatial And Temporal Variability ◽  
Eastern Region ◽  
Southwest Indian Ocean ◽  
Mesoscale Convective

Abstract A combination of numerous factors, including geographic position, regional orography, and local sea surface temperatures, means that subtropical southern Africa experiences considerable spatial and temporal variability in rainfall and is prone to both frequent flooding and drought events. One system that may contribute to rainfall variability in the region is the mesoscale convective complex (MCC). In this study, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data is used to document the precipitation produced by MCCs over southern Africa for the 1998–2006 period. Most of the rainfall associated with MCCs is found to occur over central Mozambique, extending southward to eastern South Africa. High precipitation totals associated with these systems also occur over the neighboring southwest Indian Ocean, particularly off the northeast coast of South Africa. MCCs are found to contribute up to 20% of the total summer rainfall (November–March) in parts of the eastern region of southern Africa. If the month of March is excluded from the analysis, then the contribution increases up to 24%. In general, the MCC summer rainfall contribution for most of the eastern region is approximately between 8% and 16%. Over the western interior and Botswana and Namibia, the MCC contribution is much less (<6%). It is also evident that there is considerable interannual variability associated with the contribution that these systems make to the total warm season rainfall.

scholarly journals Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

2017 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Unified Model ◽  
Seasonal Precipitation ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~ 200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

scholarly journals ENSO Teleconnection to Eastern African Summer Rainfall in Global Climate Models: Role of the Tropical Easterly Jet

2021 ◽  
Vol 34 (1) ◽  
pp. 293-312
Author(s):  
Amandeep Vashisht ◽  
Benjamin Zaitchik ◽  
Anand Gnanadesikan
Keyword(s):  
Climate Variability ◽  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Summer Rainfall ◽  
Global Climate Models ◽  
Eastern Africa ◽  
Climate Variability And Change ◽  
Tropical Easterly Jet ◽  
Interannual Rainfall Variability

AbstractGlobal climate models (GCMs) are critical tools for understanding and projecting climate variability and change, yet the performance of these models is notoriously weak over much of tropical Africa. To improve this situation, process-based studies of African climate dynamics and their representation in GCMs are required. Here, we focus on summer rainfall of eastern Africa (SREA), which is crucial to the Ethiopian Highlands and feeds the flow of the Blue Nile River. The SREA region is highly vulnerable to droughts, with El Niño–Southern Oscillation (ENSO) being a leading cause of interannual rainfall variability. Adequate understanding and accurate representation of climate features that influence regional variability is an important but often neglected issue when evaluating models. We perform a process-based evaluation of GCMs, focusing on the upper-troposphere tropical easterly jet (TEJ), which has been hypothesized to link ENSO to SREA. We find that most models have an ENSO–TEJ coupling similar to observed, but the models diverge in their representation of TEJ–SREA coupling. Differences in the latter explain the majority (80%) of variability in ENSO teleconnection simulation across the models. This is higher than the variance explained by rainfall coupling with the Somali jet (44%) and African easterly jet (55%). However, our diagnostics of the leading hypothesized mechanism in the models—variability in divergence in the TEJ exit region—are not consistent across models and suggest that a deeper understanding of the mechanisms of TEJ–precipitation coupling should be a priority for studies of climate variability and change in the region.

scholarly journals Systematic Climate Model Rainfall Biases over Southern Africa: Links to Moisture Circulation and Topography

2018 ◽  
Vol 31 (18) ◽  
pp. 7533-7548 ◽  
Author(s):  
C. Munday ◽  
R. Washington
Keyword(s):  
Southern Africa ◽  
Climate Models ◽  
Climate Model ◽  
Regional Scale ◽  
Model Development ◽  
Coupled Model ◽  
Climate Science ◽  
Summer Rainfall ◽  
Southwest Indian Ocean ◽  
Low Level

An important challenge for climate science is to understand the regional circulation and rainfall response to global warming. Unfortunately, the climate models used to project future changes struggle to represent present-day rainfall and circulation, especially at a regional scale. This is the case in southern Africa, where models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) overestimate summer rainfall by as much as 300% compared to observations and tend to underestimate rainfall in Madagascar and the southwest Indian Ocean. In this paper, we explore the climate processes associated with the rainfall bias, with the aim of assessing the reliability of the CMIP5 ensemble and highlighting important areas for model development. We find that the high precipitation rates in models that are wet over southern Africa are associated with an anomalous northeasterly moisture transport (~10–30 g kg−1 s−1) that penetrates across the high topography of Tanzania and Malawi and into subtropical southern Africa. This transport occurs in preference to a southeasterly recurvature toward Madagascar that is seen in drier models and reanalysis data. We demonstrate that topographically related model biases in low-level flow are important for explaining the intermodel spread in rainfall; wetter models have a reduced tendency to block the oncoming northeasterly flow compared to dry models. The differences in low-level flow among models are related to upstream wind speed and model representation of topography, both of which should be foci for model development.

scholarly journals Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

2018 ◽  
Vol 11 (5) ◽  
pp. 1823-1847 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Seasonal Precipitation ◽  
Physical Mechanisms ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyse the sensitivity to air–sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ∼ 200, 90 and 40 km in the zonal direction at the equator, respectively) are analysed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China but improve with finer resolution and coupling. Empirical orthogonal teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air–sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal mean time series. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

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