Tag: Climate Change Downscaling Techniques

Abstract

This paper has investigated the impact of convective parameterization schemes (CPS) and land surface models (LSM) on the simulation of summer climate over the Democratic People’s Republic of Korea (DPR Korea) using the regional climate model (RegCM 4.7). The sensitivity experiments with two LSMs [Biosphere Atmosphere Transfer Scheme (BATS) and Community Land Model (CLM 3.5)] and four CPSs (Grell, Emanuel, Grell over land and Emanuel over ocean (GL_EO), Emanuel over land and Grell over ocean (EL_GO)) at 30 km horizontal resolution are carried out in summer (from June to August) for 10 years (2001–2010) for this purpose. The simulation results are compared with the available observation data provided from the State Hydro-Meteorological Administration of the DPR Korea (SHMAK). The results show that summer mean circulation patterns (SMCP) and summer averaged surface temperature (SAST) is well captured for most of the simulations, but summer rainfall is not well represented by RegCM 4.7. The performance of the CLM3.5 scheme is better in all the simulations than the BATS scheme. Among the CPSs, the EL_GO scheme shows the smallest biases in the simulation of SAST and summer rainfall. The simulations using EL_GO with CLM3.5 shows the best performance in simulating the SAST and summer rainfall over the study region among the considered CPSs and LSMs. These results will be helpful to improve the prediction of climate change over the DPR Korea.

Abstract

Near-surface air temperature is an essential climate variable for the study of many biophysical phenomena, yet is often only available as a daily mean or extrema (minimum, maximum). While many applications require sub-diurnal dynamics, temporal interpolation methods have substantial limitations and atmospheric reanalyses are complex models that typically have coarse spatial resolution and may only be periodically updated. To overcome these issues, we developed an hourly air temperature product for Australia with spatial interpolation of hourly observations from 621 stations between 1990 and 2019. The model was validated with hourly observations from 28 independent stations, compared against empirical temporal interpolation methods, and both regional (BARRA-R) and global (ERA5-Land) reanalysis outputs. We developed a time-varying (i.e., time-of-day and day-of-year) coastal distance index that corresponds to the known dynamics of sea breeze systems, improving interpolation performance by up to 22.4% during spring and summer in the afternoon and evening hours. Cross-validation and independent validation (n = 24/4 OzFlux/CosmOz field stations) statistics of our hourly output showed performance that was comparable with contemporary Australian interpolations of daily air temperature extrema (climatology/hourly/validation: R² = 0.99/0.96/0.92, RMSE = 0.75/1.56/1.78 °C, Bias = -0.00/0.00/-0.03 °C). Our analyses demonstrate the limitations of temporal interpolation of daily air temperature extrema, which can be biased due to the inability to represent frontal systems and assumptions regarding rates of temperature change and the timing of minimum and maximum air temperature. Spatially interpolated hourly air temperature compared well against both BARRA-R and ERA5-Land, and performed better than both reanalyses when evaluated against the 28 independent validation stations. Our research demonstrates that spatial interpolation of sub-diurnal meteorological fields, such as air temperature, can mitigate the limitations of alternative data sources for studies of near-surface phenomena and plays an important ongoing role in supporting numerous scientific applications.

Abstract

Decomposing the responses of ecosystem structure and function in drylands to changes in human-environmental forcing is a pressing challenge. Though trend detection studies are extensive, these studies often fail to attribute them to potential spatiotemporal drivers. Most attribution studies use a single empirical model or a causal graph that cannot be generalized or extrapolated to larger scales or account for spatial changes and multiple independent processes. Here, we proposed and tested a multi-stage, multi-model framework that detects vegetation trends and attributes them to ten independent social-environmental system (SES) drivers in Kazakhstan (KZ). The time series segmented residual trend analysis showed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km²), followed by climate change and climate variability. Pixel-wise fitted Granger Causality and random forest models revealed that sheep & goat density and snow cover had dominant negative and positive impacts on vegetation in degraded areas, respectively. Overall, we attribute vegetation changes to SES driver impacts for 19.81% of KZ (out of 2.39 million km²). The identified vegetation degradation hotspots from this study will help identify locations where restoration projects could have a greater impact and achieve land degradation neutrality in KZ.

Abstract

Context

There is a gap of knowledge on the effects of fine resolution landcover patterns on the distribution of air temperatures within neighborhoods, as well as on how these effects may differ depending on temporal (i.e., summer and winter, diurnal and nocturnal), and spatial (i.e. extent) scales.

Objectives

(1) Evaluate the effects of compositional and configurational fine-scale landcover patterns on the spatial distribution of air temperatures within neighborhoods. (2) Determine differences between winter and summer seasons and diurnal and nocturnal periods. (3) Evaluate if these effects relate to the spatial extent used for the analysis.

Methods

Relationships between four landscape metrics and air temperature within four contrasting neighborhoods located in Santiago. Landcover was classified in six classes (built up, barren, grass, evergreen, deciduous, woody) from 1.5m resolution satellite images and temperature acquired from dataloggers located within neighborhoods. Linear mixed models were used for testing the relationships at six spatial extents.

Results

Landcover composition and configuration influence temperatures within neighborhoods, but these effects can greatly differ depending on the season, time of the day and extent of analysis. Grass and evergreen trees show the highest effects on neighborhood´s temperatures among the six landcover classes. Grass reduces summer temperatures at smaller extents but may increase temperatures at larger extents. Evergreen trees play a major role during the winter season increasing coldest nocturnal temperatures at all the analyzed extents. These vegetation effects appear to be mostly associated with the average and largest size of their respective patches.

Conclusions

Fine-scale landcover patterns play a role in regulating temperatures within neighborhoods, but these effects depend on the season, time of the day and spatial extent. Researchers and decision makers must be aware that results obtained at a given scale cannot be directly translated to another scales.

Abstract

In order to evaluate the performance of the Regional Climate Model version 4.7 (RegCM4.7) and understand the impact of land surface schemes in simulating precipitation and temperature over Iran, two thirty-year simulations were conducted using the Biosphere-Atmosphere Transfer Scheme (BATS) and the Community Land Model version 4.5 (CLM4.5). The boundary and initial conditions data of the MPI-ESM1.2-HR Earth system model were downscaled from an initial resolution of 100 × 100 km to 30 × 30 km. Both schemes were assessed against ECMWF Reanalysis v5 (ERA5) data, with temperature prediction using the BATS scheme generally reducing bias, except in spring. The CLM4.5 model exhibited a high correlation with ERA5 data, particularly in winter. Evaluation using Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency, and Kling-Gupta efficiency indices favored the CLM4.5 model in spring and winter. However, the annual temperature correlation coefficient between the two schemes showed minimal difference. In order to enhance precipitation simulation, the common linear scaling bias correction method was modified. Precipitation simulation demonstrated improved accuracy with Modified Linear Scaling (MLS) bias correction method, with the BATS scheme showing reduced bias and lower error rates. While the Kling-Gupta and Nash-Sutcliffe indices slightly favored the BATS scheme, the difference was marginal. Conversely, the Normalized RMSE (NRMSE) index favored RegCM-CLM4.5 in spring and winter. The values of the correlation coefficient and the relative standard deviation resulting from the two land surface schemes (models) had negligible differences with each other. Overall, Taylor diagram analysis suggested similar performance of both schemes at these scales.

Abstract

Communicating climate change projections to diverse stakeholders and addressing their concerns is crucial for fostering effective climate adaptation. This paper explores the use of storyline projections as an intermediate technology that bridges the gap between climate science and local knowledge in the Pilcomayo basin. Through fieldwork and interviews with different stakeholders, key environmental concerns influenced by climate change were identified. Traditional approaches to produce regional climate information based on projections often lack relevance to local communities and fail to address their concerns explicitly. By means of storylines approach to evaluate climate projections and by differentiating between upper and middle-lower basin regions and focusing on dry (winter) and rainy (summer) seasons, three qualitatively different storylines of plausible precipitation and temperature changes were identified and related to the main potential risks. By integrating these climate results with local knowledge, a summary of the social and environmental impacts related to each storyline was produced, resulting in three narrated plausible scenarios for future environmental change. The analysis revealed that climate change significantly influences existing issues and activities in the region. Projected trends indicate a shift towards warmer and drier conditions, with uncertainties mainly surrounding summer rainfall, which impacts the probability of increased flooding and river course changes, two of the most concerning issues in the region. These findings serve as a foundation for problem-specific investigations and contribute to informed decision-making for regional climate adaptation. Finally, we highlight the importance of considering local concerns when developing climate change projections and adaptation strategies.

Abstract

The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region.

Abstract

The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region.

Abstract

Accurate forecasting of the temporal distribution pattern of sub-daily precipitation is of paramount importance for effective flood control design and early warning systems. This study focuses on improving the accuracy of such forecasts by employing post-processing techniques. The European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation product over Iran was adopted along with three post-processing methods including Quantile Mapping (QM), Support Vector Machine (SVM), and Random Forest (RF). The accuracy of the forecasts for various precipitation temporal characteristics, including the start, duration, and end of precipitation events were evaluated. The RF method proved to be the most effective in improving forecast accuracy, especially in regions with higher precipitation rates. Additionally, RF corrected the first quartile of precipitation forecasts across all precipitation regions, significantly enhancing forecast accuracy in regions 3 and 5 of Iran. As for the temporal distribution pattern, post-processing methods improved the accuracy of the forecasts across all regions. The QM method performed better in terms of distributing precipitation amounts among quartiles. Moreover, all post-processing methods showed a high degree of similarity between observed and forecasted temporal distribution patterns. The deterministic evaluation showed that RF outperforms other methods in enhancing the accuracy of most precipitation quartiles, particularly that of the third quartile. The SVM and QM methods showed mixed performances, improving accuracy in some quartiles but performing adversely in others. Overall, this research highlighted the importance of data post-processing in enhancing the accuracy of precipitation forecasts and their temporal distribution patterns. The RF method proved to be the most effective post-processing technique. These findings have significant implications for flood forecasting and management in regions prone to extreme precipitation events.

Abstract

The Weather Research & Forecasting Model (WRF, Version 4.4) was applied to simulate meteorological conditions in the city of Quito, Ecuador, located in a tropical Andean landscape. These simulations included the urban canopy into WRF, using the Building Environment Parameterization (BEP) scheme combined with Local Climate Zones (LCZ) land use classification; the innermost domain had a horizontal resolution of 2 km. The simulation results showed that using LCZ + BEP options improved the representation of wind speed and planetary boundary layer height (PBLH), in comparison with WRF counter fact simulations which did not use BEP. For temperature and relative humidity, implementation of LCZ did not improve WRF simulations with respect to those counter fact simulations. This may be ascribed to the use of the default LCZ thermophysical parameters, suggesting the need for gathering local built environment features. The best WRF configuration found for wind speed was the one that combined BEP scheme, LCZ land use and the Yonsei University (YSU) PBL model with topographic option activated; this happened for dry and wet seasons and for the unique meteorological conditions in December. Regarding PBLH modeling, the best configurations were YSU-BEP-LCZ (December), MYJ-BEP-LCZ (April, wet season) and YSU (August, dry season). The findings showed the major influence of urban canopy — described by LCZ — on wind circulation and PBLH simulated within the city at high horizontal resolution (2 km). This effect should be considered when modeling atmospheric pollutant dispersion, choosing urban development strategies, and analyzing prospective climate change scenarios, among other goals.