Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments

Journal of Water and Climate Change, Journal Year: 2023, Volume and Issue: 14(7), P. 2085 - 2102

Published: June 12, 2023

Abstract Quantifying climate change impact on water resources systems at regional or catchment scales is important in planning and management. General circulation models (GCMs) represent our main source of knowledge about future change. However, several key limitations restrict the direct use GCM simulations for resource assessments. In particular, presence systematic bias need its correction an essential pre-processing step that improves quality simulations, making assessments more robust believable. What exactly bias? Can be quantified if model asynchronous with observations other simulations? Should sub-categorized to focus individual attributes interest aggregated lower moments alone? How would one address multiple without complex? could confident corrected yet-to-be-seen bear a closer resemblance truth? can meaningfully extrapolate dimensions, being impacted by ‘Curse Dimensionality’? These are some questions we attempt paper.

Language: Английский

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A software for correcting systematic biases in RCM input boundary conditions

Environmental Modelling & Software, Journal Year: 2023, Volume and Issue: 168, P. 105799 - 105799

Published: Aug. 10, 2023

Bias-correction approaches have been widely applied to Global Climate Model (GCM) or Regional (RCM) outputs in order overcome the limitations of climate models resolving small-scale features. Although various software toolkits developed simplify process for correcting model output directly, they were specifically designed correct surface fields such as precipitation and temperature, often overlooking physical mechanisms between variables. To address these limitations, this study open-source Python that corrects RCM input boundary variables using reanalysis raw GCM datasets inputs. The bias correction technique used is based on a novel approach, Sub-Daily Multivariate Bias Correction (SDMBC), which inter-variable relationships distribution atmospheric at sub-daily time scale. This paper describes package, simplifies implementation process, provides simple example its application.

Language: Английский

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Can Sub‐Daily Multivariate Bias Correction of Regional Climate Model Boundary Conditions Improve Simulation of the Diurnal Precipitation Cycle?

Geophysical Research Letters, Journal Year: 2023, Volume and Issue: 50(22)

Published: Nov. 20, 2023

Abstract The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly terms of rainfall frequency and amplitude. While improvements the regional (RCM) with bias‐corrected boundaries have been reported previous studies, they assumed that patterns are simulated correctly by GCM, potentially leading to inaccuracies maximum timing magnitude within RCM domain. Here we provide first examination cycle, a domain, achieved through use sophisticated lateral lower boundary conditions. Results show RCMs generally present improvement capturing both magnitude, northern Australia, where strong pattern prevalent. We correcting systematic sub‐daily multivariate bias improves which important regions short‐term intense precipitation occurs.

Language: Английский

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Assessment of the Climate Change Impact on Broiler Chickens in Northern Tunisia

Brazilian Journal of Poultry Science, Journal Year: 2024, Volume and Issue: 26(1)

Published: Jan. 1, 2024

Climate change continues to influence global ecosystems, raising concerns for livestock. This study assesses the impacts of climate on broiler chickens in northern Tunisia, focusing well-being and mortality rates during summer. Historical data from NRMCM5.1 MPIESM1.2 models, were utilized, covering 1970 1997. Projections 2041-2070 under RCP4.5 RCP8.5 emissions scenarios examined, providing insight into future challenges. The Temperature-Humidity Index (THI) Temperature-Humidity-Velocity (THVI) served as thermal comfort indicators. research utilized temperature relative air humidity two models (RCP4.5 RCP8.5) inputs DCP system, thus evaluating parameters (THI THVI). analysis involved calculating annual averages at system's output each grid region. projected employed assess levels by identifying heatwave periods, which had an average duration 2.7 consecutive days with THI exceeding 30.6°C. showed significant increases THVI pessimistic scenario, indicating a risk heat stress. Mortality used measure vulnerability poultry industry change, projections substantial 2.2°C 1.5°C THVI.. predicted increase period 2041-2070, increasing 0.8 1.3 0.6 1.1 RCP4.5, highlighting need adaptation strategies ensure sustainability farming.

Language: Английский

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Investigation of Uncertainties in Multi-variable Bias Adjustment in Multi-model Ensemble

Proceedings of the International Association of Hydrological Sciences, Journal Year: 2024, Volume and Issue: 386, P. 55 - 60

Published: April 19, 2024

Abstract. Post-processing methods such as univariate bias adjustment have been widely used to reduce the in individual variable. These are applied variables independently without considering inter-variable dependence. However, compound events, multiple atmospheric factors occur simultaneously or succession, leading more severe and complex impacts. Therefore, a multi-variable is necessary retain dependence between drivers. The present study focuses on of surface air temperature relative humidity multi-model ensemble. We investigated added values biases before after adjusting variables. There gains losses throughout process adjustment. effectively reduces temperature; however, it shows amplification for at higher altitudes. Added were improved lower altitudes but showed reductions Overall, improvement reducing over low-altitude urban areas, encouraging its application assess events. findings highlight potential approach regions with constraint observational data.

Language: Английский

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Correcting Multivariate Biases in RCM Boundaries: How are Synoptic Systems impacted over the Australian Region?

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: July 25, 2024

Synoptic climatology, which connects atmospheric circulation with regional environmental conditions, is pivotal to understanding climate dynamics. While models (RCMs) can reproduce key mesoscale precipitation patterns, biases related synoptic from the driving model, typically global (GCMs), often remain unaddressed. This study examines influence of correcting systematic bias in RCM boundaries on representation Australian systems. We utilize a structural self-organizing map (SOM) evaluate frequency, persistence, and transitions daily Our findings reveal that an multivariate bias-corrected improves systems compared GCM, or uncorrected simply boundaries, particularly reference frequency identified. demonstrates appropriately boundary conditions helps correct many errors inherited GCM but not all.

Language: Английский

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Impact of Assimilating Geostationary Interferometric Infrared Sounder Observations from Long- and Middle-Wave Bands on Weather Forecasts with a Locally Cloud-Resolving Global Model

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(18), P. 3458 - 3458

Published: Sept. 18, 2024

The Geostationary Interferometric InfraRed Sounder (GIIRS) provides a novel opportunity to acquire high-spatiotemporal-resolution atmospheric information. Previous studies have demonstrated the positive impacts of assimilating GIIRS radiances from either long-wave temperature or middle-wave water vapor bands on modeling high-impact weather processes. However, impact both forecast skill has been less investigated, primarily due non-identical geolocations for bands. In this study, locally cloud-resolving global model is utilized assess observations and findings indicate that exhibit distinct inter-channel error correlations. Proper inflation these errors can compensate inaccuracies arising treatment geolocation two bands, leading significant enhancement in usage assimilation not only markedly reduces normalized departure standard deviations most channels independent instruments, but also improves states, especially forecasting, with maximum reduction 42% root-mean-square lower troposphere. These improvements contribute better performance predicting heavy rainfall.

Language: Английский

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Co-Development of Deep Learning and Process-Based Eco-Hydrological Models for Enhanced Climate Resilience

Published: Jan. 1, 2024

Language: Английский

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Correcting Multivariate Biases in Regional Climate Model Boundaries: How Are Synoptic Systems Impacted Over the Australian Region?

Geophysical Research Letters, Journal Year: 2024, Volume and Issue: 51(21)

Published: Nov. 8, 2024

Abstract Synoptic climatology, which connects atmospheric circulation with regional environmental conditions, is pivotal to understanding climate dynamics. While models (RCMs) can reproduce key mesoscale precipitation patterns, biases related synoptic from the driving model, typically global (GCMs), often remain unaddressed. This study examines influence of correcting systematic bias in RCM boundaries on representation Australian systems. We utilize a structural self‐organizing map evaluate frequency, persistence, and transitions daily Our findings reveal that an multivariate bias‐corrected improves systems compared GCM, or uncorrected simply boundaries, particularly reference frequency identified. demonstrates appropriately boundary conditions helps correct many errors inherited GCM but not all.

Language: Английский

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Hydrological Response to Climate Change: McGAN for Multi-Site Scenario Weather Series Generation and LSTM for Streamflow Modeling

Atmosphere, Journal Year: 2024, Volume and Issue: 15(11), P. 1348 - 1348

Published: Nov. 9, 2024

This study focuses on the impacts of climate change hydrological processes in watersheds and proposes an integrated approach combining a weather generator with multi-site conditional generative adversarial network (McGAN) model. The incorporates ensemble GCM predictions to generate regional average synthetic series, while McGAN transforms these averages into spatially consistent data. By addressing spatial consistency problem generating this tackles key challenge site-scale impact assessment. Applied Jinghe River Basin west-central China, generated daily temperature precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up 2100. These were then used long short-term memory (LSTM) network, trained historical data, simulate river flow from 2021 results show that (1) effectively addresses correlation generation; (2) future is likely increase flow, particularly high-emission scenarios; (3) frequency extreme events may increase, proactive policies can mitigate flood drought risks. offers new tool hydrologic–climatic assessment studies.

Language: Английский

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