Tag: Climate Change Downscaling Techniques

Abstract

The Tibetan Plateau faces changing precipitation and environmental conditions affecting alpine ecosystems and downstream freshwater sustainability. While aerosol influence has been highlighted, how human-induced greenhouse warming impacts the plateau’s moisture recycling remains unclear. Here we show that the Tibetan Plateau’s recent precipitation changes result from enhanced precipitation recycling and moisture convergence that offset the decline in monsoon- and westerly-associated moisture transport based on 40-year Lagrangian simulations and water budget analyses. Local evapotranspiration is observed to increase faster in percentage than precipitation, a trend expected to continue in future warming scenarios according to climate projections. Greenhouse gas emission causes widespread wetting while weakening the southerly monsoons across the Himalayas, heightening the sensitivity of precipitation to evapotranspiration and thereby local land surface changes. This trend exacerbates vulnerability in the water cycle of high mountain Asia, calling for proactive management to address potential risks and ensure future water and food security in Asia.

Abstract

The near-term performance efficiency in predicting the dry and wet years in the West African monsoon (WAM) season (May to October) has been studied from 1979 to 2050 using the CRU observational rainfall, NCEP RII atmospheric circulation fields, and CORDEX-Africa outputs in the historical and RCP 8.5 experiments. The dry and wet years from the 6-month SPI at the Western Sudano Sahel (WSS), Eastern Sudano Sahel (ESS), and Guinea Coast (GC) rainfall regions, respectively, have shown consistency in the associated features during such WAM season extremes. The ensemble mean of the historical outputs (1979 to 2005) shows varying simulations of the WAM season; non-significant correlation in rainfall in GC and its overestimation at the three regions, the underestimating (overestimating) of Moisture Flux Convergence (MFC) in the Sahel (GC), and the “non-reasonable” performance by the Kling-Gupta efficiency in simulating the zonal moisture flux in GC and meridional moisture flux in ESS. The bias-correction of the RCP 8.5 outputs has shown improved performance efficiency of the models simulations from 2006 to 2021, however, with the attendant limitations in the technique. The bias-corrected rainfall showed underestimation at all regions although indicating negative significant correlation at the GC (r = -0.33, at 99.9% Confidence level from t-test) whereas the MFC has shown reasonable performance in the GC (KGE = -0.39). However, the ensemble mean of the models presents greater efficiency in projecting the WAM dry and wet years although there are yet huge uncertainties in the projections indicated by the MBE values. The 6-month SPI projections from the improved RCP 8.5 simulation present 2048 to be dry and 2035, 2042 and 2047 to be wet years during the WAM from 2022 to 2050. Noteworthy is the impact of MFC on rainfall being consistent in both the historical and the bias-corrected models’ outputs, having a greater impact by 2050.

Abstract

The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies.

Abstract

The Tibetan Plateau (TP) region, also known as the “Asian water tower”, provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d⁻¹ (−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.

Abstract

The Tibetan Plateau (TP) region, also known as the “Asian water tower”, provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d⁻¹ (−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.

Abstract

Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R<1, mm h⁻¹) accounts for 86% of the total precipitation time, while small raindrops (D<2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in Yangbajain (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relationships between the slope Λ and shape μ, D_m and N_w of gamma DSDs were derived. Additionally, we established a correlation between the equivalent diameter and drop axis ratio and found that raindrops on the TP attain a nearly spherical shape. Consequently, the application of the rainfall retrieval algorithms of the dual-frequency precipitation radar in the TP is improved based on the statistical results of the DSD.

Abstract

Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R<1, mm h⁻¹) accounts for 86% of the total precipitation time, while small raindrops (D<2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in Yangbajain (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relationships between the slope Λ and shape μ, D_m and N_w of gamma DSDs were derived. Additionally, we established a correlation between the equivalent diameter and drop axis ratio and found that raindrops on the TP attain a nearly spherical shape. Consequently, the application of the rainfall retrieval algorithms of the dual-frequency precipitation radar in the TP is improved based on the statistical results of the DSD.

Abstract

Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for large-scale LST retrieval. It explores the impact of Spectral indices of the surface parameters, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to reduce uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF’s superior performance in LST estimations during both summer and winter seasons. All the ML models gave an R-square of above 0.8 and RF with slightly higher R-square during both summer (0.93) and winter (0.85). Building on these findings, the study extends its focus to Ranchi, demonstrating RF’s robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth’s dynamic systems.

Abstract

Climate change has adversely impacted nature-based value chains in the arid and semi-arid regions. The study investigates the impact of climate change on nature-based value chains in arid and semi-arid environments. With an emphasis on rangeland adaptation in Kenya, the study used the VC-ARID (Value Chains for Arid and Semi-Arid Regions) approach, designed to build climate-resilient value chains in arid and semi-arid regions. The findings reveal that climate change poses significant risks to nature-based value chains, including reduced water availability, increased temperature variability, and changes in precipitation patterns. The study identifies several challenges to value chain development, such as limited access to climate information, financial resources, and supportive policies. It underscores the urgent need to integrate climate resilience into value chain interventions to achieve poverty reduction and food security goals in arid and semi-arid regions. This calls for active collaboration and investment in climate information services, research, and extension services to enhance the adaptive capacity of value chain actors and promote sustainable development in vulnerable regions. Given that climate hazards and changes are context-specific, strategies for improving value chains' sustainability must be customized to the unique ecological and socioeconomic setting in which the nature-based value chain operates. Consequently, to build value chains that are climate resilient, each actor along the chain must assess climate risks and manage the activities that make up the value chain adaptively.

Abstract

The Mediterranean basin is recognized as a potential focal point of global warming, marked by a rising incidence of both droughts and floods. Nevertheless, uncertainties persist regarding the precise impact of climate change on the water cycle in this region. Therefore, this study endeavors to scrutinize the recent precipitation trends and fluctuations across the Maghreb (Morocco, Algeria, and Tunisia) and their connection to both the Atlantic and Mediterranean moisture sources. To spatialize these trends and variations, we used a satellite precipitation product that we have beforehand evaluated at different time scales. Furthermore, we employed two influential teleconnection patterns: the NAO index, representing Atlantic influence, and the WeMO index for theMediterranean influenceThe statistical assesment of the satellite-based rainfall data demonstrated strong correlations with ground-based rainfall, ranging from 0.45 to 0.8. The median Percentage Bias was found to be 10%. The median Mean Absolute Error was approximately 12 mm, while the Root Mean Square Error averaged around 18 mm. Overall, all chosen criteria yielded satisfactory outcomes, providing a suitable level of confidence for conducting spatio-temporal trend analysis at the pixel level. At temporal scale, the trend results showed some upward trends in precipitation in certain areas during the months of March, April, August, September, and October. However, for the remainder of the year, the dominant trend is a decrease in precipitation across most of the North African territories. At spatial scale, the findings unveiled a decline in precipitation levels in the central and southern regions, while showcasing an increase in precipitation across the northern Maghreb. Moreover, the sphere of influence exerted by the WeMO exhibited expansion, along with a notable amplification in its modulation of precipitation patterns, particularly from September to April. Conversely, the NAO exerted a more pronounced influence during the winter months.