Tag: Climate Change Downscaling Techniques

Abstract

It is crucial to investigate the characteristics of fire danger in the Beijing-Tianjin-Hebei (BTH) region to improve the accuracy of local fire danger monitoring, forecasting, and management. With the use of instrumental observation data from 173 national meteorological stations in the BTH region from 1991 to 2020, the fire weather index (FWI) is first calculated in this study, and its spatiotemporal characteristics are analyzed. The high- and low-fire danger periods based on the FWI occur in April and August, respectively, with significant decreasing and increasing trends throughout the BTH region over the past 30 years. Next, the contributions of different meteorological factors to the FWI are quantified via a detrending technique. Most regions are affected by precipitation during the high-fire danger period. Both the maximum surface air temperature (Tmax) and precipitation, however, notably contribute to the FWI trend changes during the low-fire danger period. Then, we assess the linkage with atmospheric circulation. Abundant water vapor from the Northwest Pacific and local upward motion jointly lead to increased precipitation and, as a consequence, a decreased FWI during the high-fire danger period. A lack of water vapor from the boreal zone and local downward movement could cause adiabatic subsidence and hence, amplify the temperature and FWI during the low-fire danger period. In contrast to shared socioeconomic pathway (SSP) 585, in which the FWI in the BTH region exhibits a north–south dipole during the low-fire danger period, SSP245 yields an east–west dipole during the low-fire danger period. This study reveals that there is a higher-than-expected probability of fire danger during the low-fire danger period. Therefore, it is essential to intensify research on the fire danger during the low-fire danger period to improve our ability to predict summer fire danger.

Abstract

Selecting the best region-specific climate models is a precursor information for quantifying the climate change impact studies on hydraulic/hydrological projects and extreme heat events. A crucial step in lowering GCMs simulation-related uncertainty is identifying skilled GCMs based on their ranking. This research performed a critical assessment of 30 general circulation models (GCMs) from CMIP6 (IPCC’s sixth assessment report) for maximum and minimum temperature over Indian subcontinent. The daily temperature data from 1965 to 2014 were considered to quantify maximum and minimum temperatures using a gridded spatial resolution of 1°. The Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), Perkins skill score (PSS), normalized root mean square error (NRMSE), and absolute normalized mean bias error (ANMBE) were employed as performance indicators for two different scenarios, S1 and S2. The entropy approach was used to allocate weights to each performance indicator for relative ranking. Individual ranking at each grid was achieved using a multicriteria decision-making technique, VIKOR. The combined ranking was accomplished by integrating group decision-making, average ranking perspective, and cumulative percentage coverage of India. The outcome reveals that for S1 and S2, NRMSE and NSE are the most significant indicators, respectively whereas CC is the least significant indicator in both cases. This study identifies ensemble of KIOST-ESM, MRI-ESM2-0, MIROC6, NESM3, and CanESM5 for maximum temperature and E3SM-1-0, NESM3, CanESM5, GFDL-CM4, INM-CM5-0, and CMCC-ESM2 for minimum temperature.

Abstract

Selecting the best region-specific climate models is a precursor information for quantifying the climate change impact studies on hydraulic/hydrological projects and extreme heat events. A crucial step in lowering GCMs simulation-related uncertainty is identifying skilled GCMs based on their ranking. This research performed a critical assessment of 30 general circulation models (GCMs) from CMIP6 (IPCC’s sixth assessment report) for maximum and minimum temperature over Indian subcontinent. The daily temperature data from 1965 to 2014 were considered to quantify maximum and minimum temperatures using a gridded spatial resolution of 1°. The Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), Perkins skill score (PSS), normalized root mean square error (NRMSE), and absolute normalized mean bias error (ANMBE) were employed as performance indicators for two different scenarios, S1 and S2. The entropy approach was used to allocate weights to each performance indicator for relative ranking. Individual ranking at each grid was achieved using a multicriteria decision-making technique, VIKOR. The combined ranking was accomplished by integrating group decision-making, average ranking perspective, and cumulative percentage coverage of India. The outcome reveals that for S1 and S2, NRMSE and NSE are the most significant indicators, respectively whereas CC is the least significant indicator in both cases. This study identifies ensemble of KIOST-ESM, MRI-ESM2-0, MIROC6, NESM3, and CanESM5 for maximum temperature and E3SM-1-0, NESM3, CanESM5, GFDL-CM4, INM-CM5-0, and CMCC-ESM2 for minimum temperature.

Abstract

The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO₂ concentrations. It also interacts with other ecological stresses like as degradation, defaunation, and fragmentation.Ecology and climate monitoring are critical to understanding the complicated interactions between ecosystems and changing climate trends. This review paper dives into the issues of ecological and climate monitoring, emphasizing the complications caused by technical limits, data integration, scale differences, and the critical requirement for accurate and timely information. Understanding the ecological dynamics of these climatic impacts, identifying hotspots of susceptibility and resistance, and identifying management measures that may aid biosphere resilience to climate change are all necessary. At the same time, ecosystems can help with climate change mitigation and adaptation. The processes, possibilities, and constraints of such nature-based climate change solutions must be investigated and assessed. Addressing these issues is critical for developing successful policies and strategies for mitigating the effects of climate change and promoting sustainable ecosystem management. Human actions inscribe their stamp in the big narrative of our planet’s story, affecting the very substance of the global atmosphere. This transformation goes beyond chemistry, casting a spell on the physical characteristics that choreograph Earth’s brilliant dance. These qualities, like heavenly notes, create a song that echoes deep into the biosphere. We go on a journey via recorded tales of ecological transformation as they respond to the ever-shifting environment in this text. We peek into the rich fabric of change, drawing insight from interconnected observatories. Nonetheless, this growing symphony is set to unleash additional transformational stories - narratives of natural riches and rhythms that are both economically and environmentally essential. Understanding these stories is essential for navigating this developing epic. A roadmap for sustainable development necessitates the ability to comprehend these stories, a problem that resonates across the breadth of monitoring programs, particularly in the infancy of integrated sites.

Abstract

The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO₂ concentrations. It also interacts with other ecological stresses like as degradation, defaunation, and fragmentation.Ecology and climate monitoring are critical to understanding the complicated interactions between ecosystems and changing climate trends. This review paper dives into the issues of ecological and climate monitoring, emphasizing the complications caused by technical limits, data integration, scale differences, and the critical requirement for accurate and timely information. Understanding the ecological dynamics of these climatic impacts, identifying hotspots of susceptibility and resistance, and identifying management measures that may aid biosphere resilience to climate change are all necessary. At the same time, ecosystems can help with climate change mitigation and adaptation. The processes, possibilities, and constraints of such nature-based climate change solutions must be investigated and assessed. Addressing these issues is critical for developing successful policies and strategies for mitigating the effects of climate change and promoting sustainable ecosystem management. Human actions inscribe their stamp in the big narrative of our planet’s story, affecting the very substance of the global atmosphere. This transformation goes beyond chemistry, casting a spell on the physical characteristics that choreograph Earth’s brilliant dance. These qualities, like heavenly notes, create a song that echoes deep into the biosphere. We go on a journey via recorded tales of ecological transformation as they respond to the ever-shifting environment in this text. We peek into the rich fabric of change, drawing insight from interconnected observatories. Nonetheless, this growing symphony is set to unleash additional transformational stories - narratives of natural riches and rhythms that are both economically and environmentally essential. Understanding these stories is essential for navigating this developing epic. A roadmap for sustainable development necessitates the ability to comprehend these stories, a problem that resonates across the breadth of monitoring programs, particularly in the infancy of integrated sites.

Abstract

Background

In most regions and ecosystems, soils are the largest terrestrial carbon pool. Their potential vulnerability to climate and land use change, management, and other drivers, along with soils’ ability to mitigate climate change through carbon sequestration, makes them important to carbon balance and management. To date, most studies of soil carbon management have been based at either large or site-specific scales, resulting in either broad generalizations or narrow conclusions, respectively. Advancing the science and practice of soil carbon management requires scientific progress at intermediate scales. Here, we conducted the fifth in a series of ecoregional assessments of the effects of land use change and forest management on soil carbon stocks, this time addressing the Northeast U.S. We used synthesis approaches including (1) meta-analysis of published literature, (2) soil survey and (3) national forest inventory databases to examine overall effects and underlying drivers of deforestation, reforestation, and forest harvesting on soil carbon stocks. The three complementary data sources allowed us to quantify direction, magnitude, and uncertainty in trends.

Results

Our meta-analysis findings revealed regionally consistent declines in soil carbon stocks due to deforestation, whether for agriculture or urban development. Conversely, reforestation led to significant increases in soil C stocks, with variation based on specific geographic factors. Forest harvesting showed no significant effect on soil carbon stocks, regardless of place-based or practice-specific factors. Observational soil survey and national forest inventory data generally supported meta-analytic harvest trends, and provided broader context by revealing the factors that act as baseline controls on soil carbon stocks in this ecoregion of carbon-dense soils. These factors include a range of soil physical, parent material, and topographic controls, with land use and climate factors also playing a role.

Conclusions

Forest harvesting has limited potential to alter forest soil C stocks in either direction, in contrast to the significant changes driven by land use shifts. These findings underscore the importance of understanding soil C changes at intermediate scales, and the need for an all-lands approach to managing soil carbon for climate change mitigation in the Northeast U.S.

Abstract

In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRFGCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRFGCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRFGCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Abstract

This study explores the potential of TDX InSAR data from 2011, 2017, and 2019 for estimating and mapping canopy heights in unique forest and plantations landscape in Sabah, Malaysian Borneo. The findings offer crucial insights for sustainable forest and plantation management. The methodology encompassed the SINC forest height inversion model and two machine learning (ML) models Random Forest (RF) and Symbolic Regression (SR) augmented with diverse predictor variables and height references. Training the ML models with 70% of ICESat-2 ATL08 data and validating with the remaining 30%, we achieved an out-of-bag (OOB) RMSE of 5.4 m for RF and 5.96 m for SR. The overall validation RMSEs were 6.06 m (2011 SR), 10.36 m (2017 SR), and 7.58 m (2019 RF). For specific LULC classes, accuracies ranged from 3.92 m (2011 Mangrove RF) to 6.11 m (2017 Mangrove SR) and 4.35 m (2019 Rubber RF). Field inventory data validation in 2011 and 2019 yielded RMSEs between 4.06 m and 8.69 m, with SR as the top-performing model. Spatial distribution and canopy height classes revealed non-uniform variations in 2011, with SINC overestimating. In contrast, 2017 and 2019 showed uniform height patterns, indicating an increase in canopy heights across forest and plantation LULC, particularly in the 15–20 m range for oil palm, secondary forest, acacia mangium, and rubber. Our findings highlight the potential of InSAR-based canopy height estimation and mapping for tropical forest and plantations, which also can be applied to other areas at local scales considering the LULC landscapes dynamics.

Abstract

Multi-model Ensembles (MMEs) are widely used to reduce uncertainties associated with simulations and projections of GCM/RCM.MMEs combine the results of multiple climate models to produce a more robust and reliable prediction. By considering the range of outputs from different models, MMEs can improve the overall accuracy of climate projections. Therefore, the study focused on the use of some techniques, namely Multivariate Linear Regression (MLR), Weighted Average (WA), and some ML algorithms including Least Square Support Vector Machine (LS-SVM), Random Forest, (RF) and multivariate adaptive regression splines (MARS) to develop MMEs to simulate precipitation patterns over Iran. The regional climate models (RCMs) used in this research were extracted from the South Asia Coordinated Regional Climate Downscaling Experiments (CORDEX-SA) dataset. By comparing the individual RCMs and MMEs developed using the proposed methods, it was found that MMEs improved their capabilities compared to individual RCMs in their ability to simulate precipitation patterns. Furthermore, the study revealed that the MME developed using RF (MME-RF) exhibited more consistent performance across different spatial regions compared to other methods, especially WA technique, which displayed the lowest performance in comparison to other methods. Regarding the projections of seasonal precipitation under RCP4.5 and RCP8.5 scenarios, a potential decrease (roughly 6.5%) in precipitation in the western regions during autumn season, was observed. Whereas, the southern and southeast regions of Iran in particular showed a pronounced wetting tendency during the autumn season. According to the forecasts, the maximum percentage change (PC) of precipitation in these regions is expected to increase by 13.88%.

Abstract

Consistent drought modelling under plausible shared socioeconomic–representative concentration pathways (SSP–RCPs) are crucial for effectively managing future drought risk in agricultural environments. The Western Cape (WC) is one of South Africa’s main agro-based provinces and faces a mounting threat of water insecurity due to recurrent drought. The objective of this study was to predict meteorological drought hazard for 2021–2050 based on three CMIP6 scenarios: SSP5–8.5, SSP2–4.5 and SSP1–2.6. Precipitation simulations generated by the sixth version of Model for Interdisciplinary Research on Climate (MIROC6) under the SSP5–8.5, SSP2–4.5 and SSP1–2.6 scenarios were used from fifteen stations across the six AEZs of the WC province. The Standardised Precipitation Index (SPI) was computed at 12-month timescales. Trend analysis of precipitation datasets and the SPI-values were done at p < 0.05 using the Mann–Kendall (M–K) test. The findings revealed negative precipitation trends of − 7.6 mm/year in Ceres, while positive trends of 0.3 mm/year were observed in Malmesbury. These findings indicate an improvement from − 7.8 and − 6.4 mm/year in the same regions, respectively, compared to historical trends observed between 1980 and 2020. The results suggest that in 2042 and 2044, Bredasdorp will experience − 2 < SPI < − 1.5 under the SSP2–4.5 scenarios, while Matroosberg in 2038 under the SSP5–8.5 will experience SPI > − 2. The findings of this study will assist in the development of proactive planning and implementation of drought mitigation strategies and policies aimed at reducing water insecurity in AEZs.