Peer review report 2 on “Climate model performance and change projection for freshwater fluxes: comparison for irrigated areas in Central and South Asia”

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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Improved tropospheric and stratospheric sulfur cycle in the aerosol–chemistry–climate model SOCOL-AERv2

Geoscientific Model Development ◽

10.5194/gmd-12-3863-2019 ◽

2019 ◽

Vol 12 (9) ◽

pp. 3863-3887 ◽

Cited By ~ 2

Author(s):

Aryeh Feinberg ◽

Timofei Sukhodolov ◽

Bei-Ping Luo ◽

Eugene Rozanov ◽

Lenny H. E. Winkel ◽

...

Keyword(s):

Climate Model ◽

Model Performance ◽

Stratospheric Aerosol ◽

Sulfur Cycle ◽

Sulfate Aerosol ◽

Tropospheric Chemistry ◽

Sulfur Deposition ◽

Aerosol Chemistry ◽

Deposition Fluxes ◽

Aerosol Model

Abstract. SOCOL-AERv1 was developed as an aerosol–chemistry–climate model to study the stratospheric sulfur cycle and its influence on climate and the ozone layer. It includes a sectional aerosol model that tracks the sulfate particle size distribution in 40 size bins, between 0.39 nm and 3.2 µm. Sheng et al. (2015) showed that SOCOL-AERv1 successfully matched observable quantities related to stratospheric aerosol. In the meantime, SOCOL-AER has undergone significant improvements and more observational datasets have become available. In producing SOCOL-AERv2 we have implemented several updates to the model: adding interactive deposition schemes, improving the sulfate mass and particle number conservation, and expanding the tropospheric chemistry scheme. We compare the two versions of the model with background stratospheric sulfate aerosol observations, stratospheric aerosol evolution after Pinatubo, and ground-based sulfur deposition networks. SOCOL-AERv2 shows similar levels of agreement as SOCOL-AERv1 with satellite-measured extinctions and in situ optical particle counter (OPC) balloon flights. The volcanically quiescent total stratospheric aerosol burden simulated in SOCOL-AERv2 has increased from 109 Gg of sulfur (S) to 160 Gg S, matching the newly available satellite estimate of 165 Gg S. However, SOCOL-AERv2 simulates too high cross-tropopause transport of tropospheric SO2 and/or sulfate aerosol, leading to an overestimation of lower stratospheric aerosol. Due to the current lack of upper tropospheric SO2 measurements and the neglect of organic aerosol in the model, the lower stratospheric bias of SOCOL-AERv2 was not further improved. Model performance under volcanically perturbed conditions has also undergone some changes, resulting in a slightly shorter volcanic aerosol lifetime after the Pinatubo eruption. With the improved deposition schemes of SOCOL-AERv2, simulated sulfur wet deposition fluxes are within a factor of 2 of measured deposition fluxes at 78 % of the measurement stations globally, an agreement which is on par with previous model intercomparison studies. Because of these improvements, SOCOL-AERv2 will be better suited to studying changes in atmospheric sulfur deposition due to variations in climate and emissions.

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Implications of aerosol-induced snow darkening on regional hydroclimate over the Himalayas

10.5194/egusphere-egu21-11972 ◽

2021 ◽

Author(s):

Vijayakumar Sivadasan Nair ◽

Usha Keshav Hasyagar ◽

Surendran Nair Suresh Babu

Keyword(s):

South Asia ◽

Climate Model ◽

Hydrological Cycle ◽

Monsoon Season ◽

Radiation Balance ◽

Western Himalayas ◽

Radiative Effect ◽

Anthropogenic Aerosols ◽

Snow Albedo ◽

Snow Cover Extent

The snow-covered mountains of Himalayas are known to play a crucial role in the hydrology of South Asia and are known as the &#8220;Asian water tower&#8221;. Despite the high elevations, the transport of anthropogenic aerosols from south Asia and desert dust from west Asia plays a significant role in directly and indirectly perturbing the radiation balance and hydrological cycle over the region. Absorbing aerosols like black carbon (BC) and dust deposited on the snow surface reduces the albedo of the Himalayan snow significantly (snow darkening or snow albedo effect). Using a Regional Climate Model (RegCM-4.6.0) coupled with SNow, ICe and Aerosol Radiation (SNICAR) module, the implications of aerosol-induced snow darkening on the regional hydroclimate of the Himalayas are investigated in this study. The aerosols deposited on snow shows a distinct regional heterogeneity. The albedo reduction due to aerosols shows a west to east gradient during pre-monsoon season and this results in the positive radiative effect of about 29 Wm-2, 17 Wm-2 and 5 Wm-2 over western, central and eastern Himalayas respectively. The reduction in the snow albedo also results in the sign reversal of the aerosol direct radiative effect i.e., from warming to cooling at the top of the atmosphere during pre-monsoon season. The excess solar energy trapped at the surface due to snow darkening warms the surface (0.66-1.9 K) and thus decreases the snow cover extent significantly. This results in the reduction of the number of snow-covered days by more than a month over the western Himalayas and about 10 &#8211; 15 days over the central Himalayas. The early snowmelt due to aerosol-induced snow darkening results in the increase of runoff throughout the melting season. Therefore, the present study highlights the heterogeneous response of aerosol induced snow albedo feedbacks over the Himalayan region and its impact on the snowpack and hydrology, which has further implications on the freshwater availability over the region.

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Observed and Projected Frontal Activities in East Asia

Journal of Climate ◽

10.1175/jcli-d-19-0959.1 ◽

2021 ◽

pp. 1-46

Author(s):

Chia-Chi Wang ◽

Huang-Hsiung Hsu ◽

Ying-Ting Chen

Keyword(s):

East Asia ◽

Climate Model ◽

Southern China ◽

Model Performance ◽

Moisture Flux ◽

Central China ◽

Cmip5 Models ◽

The South ◽

Front Detection ◽

Local Moisture

AbstractAn objective front detection method is applied to ERA5, CMIP5 historical, and RCP8.5 simulations to evaluate climate model performance in simulating front frequency and understand future projections of seasonal front activities. The study area is East Asia for two natural seasons, defined as winter (December 2nd –February 14th) and spring (February 15th –May 15th), in accordance with regional circulation and precipitation patterns. Seasonal means of atmospheric circulation and thermal structures are analyzed to understand possible factors responsible for future front changes.The front location and frequency in CMIP5 historical simulations are captured reasonably. Frontal precipitation accounts for more than 30% of total precipitation over subtropical regions. Projections suggest that winter fronts will decrease over East Asia, especially over southern China. Frontal precipitation is projected to decrease for 10-30%. Front frequency increases in the South China Sea and tropical western Pacific because of more tropical moisture supply, which enhances local moisture contrasts. During spring, southern China and Taiwan will experience fewer fronts and less frontal precipitation while central China, Korea, and Japan may experience more fronts and more frontal precipitation due to moisture flux from the south that enhances 𝜽𝒘 gradients.Consensus among CMIP5 models in front frequency tendency is evaluated. The models exhibit relatively high consensus in the decreasing trend over polar and subtropical frontal zone in winter and over southern China and Taiwan in spring that may prolong the dry season. Spring front activities are crucial for water resource and risk management in the southern China and Taiwan.

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