Tag: Climate Change Downscaling Techniques

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

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Abstract

Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202–2050) and FP2 (2051–2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

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Abstract

Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.

Dynamically downscaled coastal flooding in Brazil’s Guanabara Bay under a future climate change scenario

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Abstract

In recent years, extensive research has been conducted on various aspects of climate change, with particular attention given to the sea level rise (SLR) as a significant consequence of global warming. Although a general trend of positive SLR exists worldwide, regional variations in SLR rates are observed. This study aims to investigate the potential impact of SLR projected by a Coupled Model Intercomparison Project phase 5 model, under a 4.5 W m \(^{-2}\) radiative forcing stabilization scenario by 2100, on coastal flooding along the Brazilian Coast. To achieve this, an ocean numerical downscaling approach was employed using multiple nested grids with the Regional Ocean Modeling System, with a specific focus on the Guanabara Bay region. Guanabara Bay is a vital water body that receives substantial water discharges from the densely populated Rio de Janeiro metropolitan area. Two experiments were conducted simulating the present (1995–2005) and future conditions (2090–2100), and the projected changes were evaluated. The results reveal a projected SLR of 0.69 m at Fiscal Island by the end of the century, anticipating potential loss of remaining mangrove areas and the expansion and persistence of coastal flooding in important tourist destinations within the Rio de Janeiro Municipality. Overall, this study provides valuable insights into the potential impacts of SLR on coastal flooding in the Brazilian Coast, emphasizing the importance of considering regional variations in SLR rates for effective coastal management and adaptation strategies.

Framing uncertainty in water policy discourse: insights from Arizona’s Project ADD Water

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Abstract

Water resource managers contend with and leverage uncertainty in complex decision-making processes. Uncertainty has passive characteristics (context and type), but it is also actively deployed and leveraged by stakeholders via framing and discourse. These passive and active elements of uncertainty interact with each other to shape discourse, which has direct consequences for policy, especially when uncertainty is invoked to delay action in addressing pressing environmental challenges. We explore the interactions of active and passive elements of uncertainty management in a water management decision-making process in the water-limited US state of Arizona. Project ADD Water was intended to engage stakeholders in dialogue about securing water resources under existing state water management policy. Understanding how uncertainty influenced Project ADD Water has only become more important as water management under prolonged drought becomes increasingly complex in Arizona and other similar sites. In the Project ADD Water deliberations, we found examples of both disruptive and productive efforts to frame different types of uncertainty, and we use illustrative examples to explore their relevance to the policy-making process. In addition to our analysis of how stakeholders engage with uncertainty through this process, we also contribute a codebook for holistically examining uncertainty in natural resource decision-making processes beyond ADD Water and materials to structure workshops with stakeholders centered on exploring uncertainty. Our contributions strengthen our understanding of how stakeholders deploy uncertainty in decision-making processes and offer a pathway to facilitate discussions about uncertainty with stakeholders.

PMIM: generating high-resolution air pollution data via masked image modeling

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Abstract

Air pollution data provides important information on air quality, which can be used to assess the impact of atmospheric pollution on human health, the environment, and the economy, as well as to develop corresponding policies and measures to reduce pollutant emissions and improve air quality. In this paper, we propose a novel approach to improve the resolution of meteorological data via masked image modeling (PMIM) to generate high-resolution air pollution data. In order to apply the image masking modeling to process air pollution data, we convert the data format and use radial basis function visualization to generate smooth distribution maps of air pollution data. To generate high-resolution air pollution data, we design several different masking strategies and use the masked image modeling to simulate the reconstruction process from low-resolution grid data to high-resolution grid data, obtaining the reconstructed high-resolution grid images. Finally, we use the mapping relationship between the pixel colors of the reconstructed images and the air pollution data to generate high-resolution air pollution concentration data. In order to verify the effectiveness of the proposed method, we conduct comparative experiments using different masking strategies and test air pollution data of different resolutions. The results show that our method has good applicability and effectiveness in different situations.

Graphical abstract

Impact of change in land use/land cover and climate variables on groundwater recharge in a tropical river basin

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Abstract

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources.

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

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Abstract

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

Temporal and spatial aggregation of rainfall extremes over India under anthropogenic warming

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Abstract

India experienced several unprecedented floods in the recent decades. The increase in the extreme rainfall events (EREs) is the primary cause for these floods, manifesting its societal impacts. The daily downscaled and bias corrected (DBC) Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall and sea surface temperature (SST) are prepared for the Indian region and are utilized to examine the characteristics of EREs. The DBC products capture the characteristic features of EREs for the baseline period, which inspired us to assess the EREs over India in CMIP6 future projections. Consistent with the observations, DBC product shows ~ 8% of Indian land found to experienced extremely heavy rainfall associated with the long duration EREs in the baseline period. However, area and extreme rainfall thresholds are projected to increase by about 18(13)% and 58(50)%, respectively in the far future under SSP5-8.5 (SSP2-4.5) emission scenario relative to the baseline period. A two-fold-65(62)% increase in long-duration EREs compared to the short-duration EREs and substantial warming ~ 2.4(2.9) oC of Indian Ocean SSTs in the far future under SSP5-8.5 (SSP2-4.5) emission scenario compared to baseline period are reported. These findings may provide fundamental insights to formulate national climate change adaptation policies for the EREs.

ConvSRGAN: super-resolution inpainting of traditional Chinese paintings

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Abstract

Existing image super-resolution methods have made remarkable advancements in enhancing the visual quality of real-world images. However, when it comes to restoring Chinese paintings, these methods encounter unique challenges. This is primarily due to the difficulty in preserving intricate non-realistic details and capturing comple semantic information with high dimensionality. Moreover, the preservation of the original artwork’s distinct style and subtle artistic nuances further amplifies this complexity. To address these challenges and effectively restore traditional Chinese paintings, we propose a Convolutional Super-Resolution Generative Adversarial Network for Chinese landscape painting super-resolution, termed ConvSRGAN. We employ Enhanced Adaptive Residual Module to delve deeply into multi-scale feature extraction in images, incorporating an Enhanced High-Frequency Retention Module that leverages an Adaptive Deep Convolution Block to capture fine-grained high-frequency details across multiple levels. By combining the Multi-Scale Structural Similarity loss with conventional losses, our ConvSRGAN ensures that the model produces outputs with improved fidelity to the original image’s texture and structure. Experimental validation demonstrates significant qualitative and quantitative results when processing traditional paintings and murals datasets, particularly excelling in high-definition reconstruction tasks for landscape paintings. The reconstruction effect showcases enhanced visual fidelity and liveliness, thus affirming the effectiveness and applicability of our approach in cultural heritage preservation and restoration.

Localised and tree total crop loads influence trunk growth, return fruit set, yield, and fruit quality in apples

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Abstract

Localised fruit thinning strategies must be investigated to improve precision crop load management in narrow-canopy, multileader apple trees. This study aimed to determine the effects of within-leader and tree total crop load on leaders’ and trunk’s growth, fruit set, yield, and fruit quality in ‘Ruby Matilda’ apples (marketed as Pink Lady®) over three years. Different crop loads were imposed on two leaders (primary and secondary) of bi-axis trees. Leader and trunk relative growth rate, return fruit set, yield, and fruit quality parameters at harvest were measured. High within-leader crop loads led to a significant increase in yield and reductions in trunk growth, return fruit set, and deterioration of fruit quality parameters except for flesh firmness and starch index. Similar trends were observed in whole-tree relationships. High crop load in secondary leaders had moderate negative effects on trunk growth, yield, and fruit mass of primary leaders; it only marginally affected their return fruit set and had no significant effect (p > 0.05) on their fruit quality. A crop load of 6.8 fruit no. cm−2 of leader cross-sectional area was estimated to achieve a relatively consistent return fruit set within the same leader. At a whole-tree level, a similar crop load (6.9 fruit no. cm−2 of trunk cross-sectional area) produced a consistent return fruit set despite its higher variability. These crop loads produced high yields (120 and 111 t ha−1, respectively) and good quality fruit. Using individual leaders as management units is recommended to simplify operations and reduce variability.