Month: February 2024

Abstract

We investigate the influence of bias correction of Global Climate Models (GCMs) prior to dynamical downscaling using regional climate models (RCMs), on the change in climate projected. We use 4 GCMs which are bias corrected against ERA-Interim re-analysis as a surrogate truth, and carry out bias corrected and non-bias corrected simulations over the CORDEX Australasia domain using the Weather Research and Forecasting model. Our results show that when considering the effect of bias correction on current and future climate separately, bias correction has a large influence on precipitation and temperature, especially for models which are known to have large biases. However, when considering the change in climate, i.e the \(\Delta\) change (future minus current), we found that while differences between bias-corrected and non-corrected RCM simulations can be substantial (e.g. more than \(1\,^\circ\) C for temperatures) these differences are generally smaller than the models’ inter-annual variability. Overall, averaged across all variables, bias corrected boundary conditions produce an overall reduction in the range, standard deviation and mean absolute deviation of the change in climate projected by the 4 models tested, over 61.5%, 62% and 58% of land area, with a larger reduction for precipitation as compared to temperature indices. In addition, we show that changes in the \(\Delta\) change for DJF tasmax are broadly linked to precipitation changes and consequently soil moisture and surface sensible heat flux and changes in the \(\Delta\) changefor JJA tasmin are linked to downward longwave heat flux. This study shows that bias correction of GCMs against re-analysis prior to dynamical downscaling can increase our confidence in projected future changes produced by downscaled ensembles.

Abstract

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region’s agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10–20% massive decrease during pre-monsoon (March–May) and earlier ISM season months (i.e., June and July). A significant 40–70% decline in mean monsoon rainfall during the June–July months in the near future (NF; 2041–2060) has been projected compared to the historical period (1986–2005). An abrupt increase of 80–170% in mean monsoon rainfall during the post-monsoon (October–December) in the far future (FF; 2080–2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5–10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9–4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP.

Abstract

High-resolution weather data is crucial for assessing future climate change impacts on local environments, yet downscaling low-resolution Global Climate Models (GCMs) outputs and addressing associated uncertainty remain significant challenges. In this study, we propose a novel spatial weather generator using generative networks, specifically a numerical conditional deep convolutional generative adversarial network (cDCGAN), as a promising solution. The cDCGAN generates high-resolution weather data from low-resolution GCM outputs and was applied to four case areas in China under four Shared Socio-economic Pathway (SSP) scenarios. The results demonstrate the cDCGAN's accuracy, consistency, and stability, with low uncertainties. The model performs optimally in low-elevation plains and tropical regions. The cDCGAN offers advantages in uncertainty analysis over traditional downscaling methods, serving as a valuable tool for climate change analysis, response estimation, and environmental management decision-making within the spatial statistics domain.

Abstract

This study assesses the performance of 37 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) in simulating extreme precipitation in Madagascar. In this study, six extreme precipitation indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) were used, namely consecutive dry days (CDD), heavy precipitation days (R10MM), very heavy precipitation days (R20MM), maximum 5-day precipitation (RX5DAY), extremely wet days (R99P), and simple daily intensity (SDII). The performance of the model was evaluated from 1998 to 2014 against the Tropical Rainfall Measuring Mission 3B42 (TRMM) and the Global Precipitation Climatology Project (GPCP). The results show that most of the models in CMIP6 reasonably reproduce the annual precipitation cycle of the study area. The results also suggested that models from CMIP6 tend to underestimate extreme precipitation indices such as CDD, R20MM, SDII, and R99P. However, for R10MM and RX5DAY, the performance of individual models varies remarkably. By looking at the performance metrics, not a single model consistently performed well. Model performance changes with the reference data, the extreme precipitation indices, and the performance metrics. However, the findings of this study indicate that overall, multi-model ensemble mean outperforms most individual models. The study lays the basis for the analysis and projection of future extreme precipitation in Madagascar but also provides scientific evidence for the choice of models.

Abstract

The diurnal cycle of rainfall in the Lake Victoria Basin of East Africa results from the super positioning of regional circulations driven by lake/land temperature differences and topography in the presence of the large-scale flow. Analysis of a triple-nested regional model simulation with a convective-permitting inner domain shows how these elements combine to produce the observed regionality of precipitation, including diurnal cycling, in boreal fall. A single diurnal rainfall peak occurs throughout the basin, but the time of maximum rainfall varies within the basin. The rainy period over the lake begins with precipitation over the northern part of the lake near 02Z (0500 LT), so it is not simply nighttime rain driven by lake/land breezes. Onset of the rainy period occurs only when low geopotential heights over the relatively warm lake cause a southward branch of the Turkana Jet to form. The formation of the jet depends on nighttime cooling over the Eastern Rift Mountains, which acts to direct the large-scale, moist flow around the topography. Topography also plays a role in the daytime rainy period over land in the Lake Victoria Basin. Moist divergence over the lake supports convergence and precipitation over the shore regions. Precipitation rates are twice the magnitude over the eastern shore compared with the western shore because daytime warming of the high elevations of the Eastern Rift Mountains allows the large-scale easterly flow to go over the mountains. This easterly flow converges with the lake/land circulation and doubles precipitation rates over the eastern shore.