Application Of Bias Correction Research Articles

Performance of weather research forecasting model for seasonal prediction of precipitation over Indonesian maritime continent

The precipitation over the Indonesian Maritime Continent (IMC) is one of the most challenging atmospheric parameters to predict accurately. The Weather Research Forecasting (WRF) model used in this study produces overestimated predictions of precipitation intensity compared to satellite data. Therefore, it is necessary to modify the model output to minimize the bias between predictions and satellite observations. The modification includes adjusting the dynamic model parameters and settings to better reflect atmospheric conditions in the IMC region, applying bias correction techniques through a linear scaling method, aligning the monthly average of the model's output with observational data, and conducting statistical analysis in several areas within the IMC. This procedure has significantly reduced the biases and is considered acceptable for each satellite area. Based on the results of the statistical analysis and by applying the precipitation threshold criteria, the accuracy of the predictions in each observation area is quite good, ranging from 0.59 to 1.0. Precipitation with a threshold of 50 mm/day or higher exhibits good accuracy, with a minimum value of 0.59 for RI and a maximum value of 0.97 for RIII and RV. On the other hand, precipitation with a threshold of 20 mm/day or higher demonstrates very good accuracy, with values of 0.97 for RI and 1.00 for RIII to RVI.

Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia

Systematic errors in regional climate models (RCMs) hinder their implementation and lead to uncertainties in regional hydrological climate change studies. As a result, checking the accuracy of climate model simulations and applying bias correction are preliminary methods for achieving consistent findings. Therefore, identifying suitable RCM models for bias correction is important for providing reliable inputs for evaluating climate change impacts. The impacts of bias correction methods on streamflow were assessed on the Katar catchment within the Lake Ziway subbasin using coordinated regional climate downscaling experiments with a spatial resolution of 50 km (CORDEX-44) RCMs through the Integrated Hydrological Modelling System (IHMS) version 6.3. This study evaluated fourteen RCM models under five precipitation and three temperature bias correction methods for the Katar catchment. Statistical approaches, such as bias (PBIAS), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of variation (CV), the coefficient of determination (R2), and the relative volume error (RVE), are used for performance analysis. GERICS-MPI, RAC4-NOAA-2G, and CCLM4-NCCR-AFR-22 have better performances for both rainfall and temprature. The empirical cumulative distribution function (ECDF) method performed best in removing bias from the frequency-based statistics of rainfall and streamflow, followed by the power transformation (PT), distribution mapping (DM), local intensity scaling (LOCI), and linear scaling (LS) methods. Specifically, for temperature, the VARI and DM methods perform better in frequency-based statistics than the LS method. The performance of hydrological modeling is strongly affected by the selection of rainfall bias correction methods. In addition, the effect of the temperature bias correction method was not significant. The adequacy of the BCM depends on the RCM models and regional context. Therefore, the BCM implementation procedure can be adapted from region to region. This study revealed that the performance of the RCM models differed and that the errors in the RCM model outputs were reduced by the use of bias correction methods.

Comparative Analysis of Climate Change Impacts on Climatic Variables and Reference Evapotranspiration in Tunisian Semi-Arid Region

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET0). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET0 model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET0 values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET0 increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET0 are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET0 patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Evaluation and correction analysis of the regional rainfall simulation by CMIP6 over Sudan

This study utilizes satellite-based rainfall CHIRPS to evaluate GCMs-CMIP6 models over Sudan from 1985 to 2014. Overall, the GCMs of BCC-CSM2-MR, CAMS-CSM1-0, CESM2, ECEarth3-Veg, GFDL-ESM4, MIROC-ES2L, and NorESM2-MM are well reproduced in the unimodal pattern of June to September (JJAS), and hence employed to calculate Multi-Model Ensemble (MME). Then, we examine the capability of the GCMs and MME in replicating the precipitation patterns on annual and seasonal scales over Sudan using numerous ranking metrics, including Pearson Correlation Coefficient (CC), Standard Deviation (SD), Taylor Skill Score (TSS), Mean Absolute Error (MAE), absolute bias (BIAS), and, normalized mean root square error (RMSD). The results show that the MME has the lowest bias and slightly overestimates rainfall over most parts of our study domain, whilst, others (ACCESS-CM2, BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, FGOALS-f3-L, FGOALS-g3) consistently overestimate rainfall in referring to CHIRPS data, respectively, but FIO-ESM-2-0 underestimates bias value. Moreover, MIROC-ES2L and NorESM2-MM demonstrate better performance than the other models. Finally, we employed a bias correction (BC) technique, namely Delta BC, to adjust the GCMs model products through the annual and monsoon seasons. The applied bias correction technique revealed remarkable improvement in the GCMs against the observations, with an improvement of 0 - 18% over the original. However, MME and MIROC-ES2L show better performance after correction than other models.

Evaluation of NASA POWER and ERA5-Land for estimating tropical precipitation and temperature extremes

Long-term gridded climate data, NASA Prediction of Worldwide Energy Resources (NASA POWER) and the fifth generation of European ReAnalysis Land Component (ERA5-Land), are important alternatives to gauges, but their effectiveness in capturing tropical precipitation and temperature extremes has not been thoroughly studied. Therefore, this study aims to evaluate the performance of NASA POWER and ERA5-Land over the Kelantan River Basin from 1981 to 2020 with 19 gauges in the aspects of climatology characteristics, overall assessment, precipitation detection capability, probability distribution function (PDF), extreme indices calculation, and the 2014–2015 flood. The important findings are as follows: (1) NASA POWER and ERA5-Land are able to reasonably capture the climatology patterns of precipitation, maximum, and minimum temperatures, with the latter performing slightly better; (2) Both products have a better correlation with gauges over coastal regions than inland mountainous regions, but they tend to underestimate precipitation and maximum temperature and overestimate minimum temperature; (3) With high POD values and moderate FAR and CSI values, they are able to detect precipitation days more effectively than non-precipitation days; (4) The PDF assessment reveals that both products overestimate moderate precipitation (1–20 mm/day) and underestimate no/tiny (0–1 mm/day), heavy (20–50 mm/day), and violent (greater than50 mm/day) precipitation; (5) With the exception of CWD and R10mm, both products tend to significantly underestimate most of the precipitation extreme indices, while a better correlation with gauges can be found in the calculations of CDD, R10mm, R20mm, TXx, and TNn; (6) Both products have a higher correlation and a better precipitation detection ability during the 2014–2015 flood, but they significantly underestimate the amount of precipitation. The findings show that NASA POWER and ERA5-Land offer valuable climate information for locations without gauges, but it is recommended to apply bias correction prior to applying them in this tropical basin.

Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

AbstractReliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

Growth-rate measurement with type-Ia supernovae using ZTF survey simulations

Measurements of the growth rate of structures at z < 0.1 with peculiar velocity surveys have the potential of testing the validity of general relativity on cosmic scales. In this work, we present growth-rate measurements from realistic simulated sets of type-Ia supernovae (SNe Ia) from the Zwicky Transient Facility (ZTF). We describe our simulation methodology, the light-curve fitting, and peculiar velocity estimation. Using the maximum likelihood method, we derived constraints on fσ8 using only ZTF SN Ia peculiar velocities. We carefully tested the method and we quantified biases due to selection effects (photometric detection, spectroscopic follow-up for typing) on several independent realizations. We simulated the equivalent of 6 years of ZTF data, and considering an unbiased spectroscopically typed sample at z < 0.06, we obtained unbiased estimates of fσ8 with an average uncertainty of 19% precision. We also investigated the information gain in applying bias correction methods. Our results validate our framework, which can be used on real ZTF data.

Mitigating Age Biases in Resume Screening AI Models

As populations age, an increasing number of workers beyond the traditional retirement age are opting to continue working. Nevertheless, discrimination against older job seekers seeking new employment opportunities remains widespread. To address this issue, we enlisted a pool of crowdworkers to assess the resumes of IT job candidates and guess each candidate's age, race, and gender. Using this crowdsourced data, we trained an AI model and applied bias correction techniques from IBM's AI 360 and Microsoft's Fairlearn toolkits to correct for biases based on race, gender, and age. We analyzed the effectiveness of these tools in mitigating different types of bias in job hiring algorithms, explored why age may be more challenging to eliminate than other forms of bias, and discussed additional approaches to enhance fairness. Our results indicate that implicit age bias, or ageism, is prevalent in hiring decisions and more pervasive than other well-documented forms of bias, such as race and gender biases.

Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests

Canopy biophysical variables such as the fraction of canopy cover (fCOVER), fraction of absorbed photosynthetically active radiation (fAPAR), and leaf area index (LAI) are widely used for ecosystem modelling and monitoring. The Sentinel-2 mission was designed for systematic global mapping of these variables at 20 m resolution using imagery from the MultiSpectral Instrument. The Simplified Level 2 Prototype Processor (SL2P) is available as a baseline mapping solution. Previous validation over limited sites indicates that SL2P generally satisfies user requirements for all three variables over crops, but underestimates LAI over forests. In this study, Sentinel-2 fAPAR, fCOVER, and LAI products, from SL2P, were validated over 281 sites representative of most North American forest ecozones and also compared to Moderate Resolution Imaging Spectrometer (MODIS) and Copernicus Global Land Service (CGLS) products. In addition to meeting the Committee on Earth Observation Satellites Stage 3 validation requirements for these areas, our study also explores the relationship between bias in SL2P products and canopy clumping and provides empirical bias correction functions for each variable.SL2P was implemented within the Landscape Evolution and Forecasting Toolbox in Google Earth Engine both for efficiency and due to bugs in the Sentinel Application Platform implementation. SL2P was found to underestimate LAI by 20% to 50% over forests with LAI > 2; in agreement with other studies and with comparisons to MODIS and CGLS products. SL2P bias for fCOVER and fAPAR transitions from ∼0.1 at low values to ∼ − 0.1 at high values. Precision error, at one standard deviation, was ∼0.5 for LAI and slightly less than ∼0.1 for fCOVER and fAPAR. Total uncertainty was dominated by bias for LAI and was slightly greater than precision error for fCOVER and fAPAR. Target user requirements were satisfied for 51% of LAI, 37% of fCOVER and 31% of fAPAR comparisons to in-situ measurements. For all variables, accuracy exhibited weak to moderate linear relationships to clumping (r2 ≤0.52), but scatter plots indicated larger negative LAI biases over northern latitude sites where canopies exhibited greater clumping. With the exception of evergreen broadleaf forests, empirical bias correction using in-situ data reduced accuracy error by 40% for fCOVER, 57% for fAPAR and, 92% for LAI and increased the agreement rate with uncertainty requirements by up to 8%. Users of SL2P LAI over forests are recommended to apply bias correction or consider recalibrating SL2P with spatially heterogenous radiative transfer model simulations.

Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations

Abstract. Statistical bias correction (BC) is a widely used tool to post-process climate model biases in heat-stress impact studies, which are often based on the indices calculated from multiple dependent variables. This study compares four BC methods (three univariate and one multivariate) with two correction strategies (direct and indirect) for adjusting two heat-stress indices with different dependencies on temperature and relative humidity using multiple regional climate model simulations over South Korea. It would be helpful for reducing the ambiguity involved in the practical application of BC for climate modeling and end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence, which benefits the indirect correction of heat-stress indices depending on the adjustment of individual components, especially those indices relying equally on multiple drivers. On the other hand, the direct correction of multivariate indices using the quantile delta mapping univariate approach can also produce a comparable performance in the corrected heat-stress indices. However, our results also indicate that attention should be paid to the non-stationarity of bias brought by climate sensitivity in the modeled data, which may affect the bias-corrected results unsystematically. Careful interpretation of the correction process is required for an accurate heat-stress impact assessment.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran

ABSTRACT .In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

Multivariate bias correction of regional climate model boundary conditions

Improving modeling capacities requires a better understanding of both the physical relationship between the variables and climate models with a higher degree of skill than is currently achieved by Global Climate Models (GCMs). Although Regional Climate Models (RCMs) are commonly used to resolve finer scales, their application is restricted by the inherent systematic biases within the GCM datasets that can be propagated into the RCM simulation through the model input boundaries. Hence, it is advisable to remove the systematic biases in the GCM simulations prior to downscaling, forming improved input boundary conditions for the RCMs. Various mathematical approaches have been formulated to correct such biases. Most of the techniques, however, correct each variable independently leading to physical inconsistencies across the variables in dynamically linked fields. Here, we investigate bias corrections ranging from simple to more complex techniques to correct biases of RCM input boundary conditions. The results show that substantial improvements in model performance are achieved after applying bias correction to the boundaries of RCM. This work identifies that the effectiveness of increasingly sophisticated techniques is able to improve the simulated rainfall characteristics. An RCM with multivariate bias correction, which corrects temporal persistence and inter-variable relationships, better represents extreme events relative to univariate bias correction techniques, which do not account for the physical relationship between the variables.

Bias correction of surface air temperature and precipitation in CORDEX East Asia simulation: What should we do when applying bias correction?

Three groups of bias correction (BC) methods, which are linear scaling, distributional-based quantile mapping (QM) and empirical-based QM method, have been applied to correct surface air temperature and precipitation from Weather Research and Forecasting model (WRF) simulation within the second phase of Coordinated Regional Downscaling Experiment East Asia (CORDEX-EA-II) framework. WRF simulation and bias-corrected results are evaluated with gridded observations from CN05.1 and APHRODITE. The evaluation is conducted in terms of climate mean, seasonal cycle and extreme indices. Results show that WRF exhibits large biases in simulating surface air temperature and precipitation, which can be significantly reduced by bias correction. Among the three groups of BC approaches, empirical-based QM is the most comprehensive method in adjusting WRF simulation. However, when considering different time scales and regions, distributional-based QM can better correct precipitation higher than 25 mm/day than empirical-based QM, and a simple linear scaling can also show comparable skills in correcting seasonal cycles. Furthermore, based solely on statistical relationships between simulations and observations, bias correction cannot adjust circulation controlled and consecutive events. These results emphasize the importance of BC validation before climate change application. In addition, BC methods should be used carefully, considering regional climate and research subjects.

Uncertainty quantification of multi-source hydrological data products for the improvement of water budget estimations in small-scale Sakarya basin, Turkey

ABSTRACT The present study aims to improve the efficacy of water budget (WB) estimations from various hydrological data products, by (1) evaluating the uncertainties of hydrological data products, (2) merging four precipitation and six evapotranspiration products using their error variances, and (3) employing the constrained Kalman filter (CKF) method to distribute residual errors among water budget components based on their relative uncertainties. The results show that applying bias correction before the merging process improved estimations of precipitation products with decreasing root mean square error (RMSE), except Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Variable Infiltration Capacity (VIC) and bias-corrected Climate Prediction Center Morphing Technique (CMORPH) products outperformed other evapotranspiration and bias-corrected precipitation products, respectively, in terms of mean merging weights. The terrestrial water storage change is the primary reason for non-closure errors, mainly caused by the coarse resolution of Gravity Recovery and Climate Experiment (GRACE). The CKF results were insensitive to variations in uncertainties of runoff. Precipitation derived from the CKF was the best precipitation output, with the highest correlation coefficient (CC) and smallest root mean square deviation (RMSD).

Validation and Bias Correction of Monthly δ18O Precipitation Time Series from ECHAM5-Wiso Model in Central Europe

Simulated stable oxygen isotopic composition (δ18O) of precipitation from isotope-enabled GCMs (iGCMs) have gained significant visibility nowadays. This study evaluates bias correction techniques to reduce the systematic and dispersion biases of the modelled δ18O by the ECHAM5-wiso model compared to the Global Network of Isotopes in Precipitation (GNIP) observations over Central Europe. mean bias error (MBE) and Root Mean Square Error (RMSE) are substantially reduced by more than 70% and 10%, respectively, depending on the bias correction scheme, with better results for Generalized Additive Model (GAM) and linear scaling approach (SCL) methods. The bias-corrected δ18OECHAM5-wiso values successfully describe the long-term isotopic composition of precipitation and the isotopic amplitude with the best performances for the EQM method. The necessity of applying bias correction algorithms is verified by the excellent agreement between the corrected δ18OECHAM5-wiso with GNIP in high-altitude areas where ECHAM5-wiso fails to reproduce the observed isotopic variability. The results are expected to bring valuable insights into the utilization of iGCMs’ relationships in climate studies for understanding the present and past water cycle under the isotopic perspective.

Localized impacts and economic implications from high temperature disruption days under climate change

AbstractMost studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments, however, are measures of the localized impact, and also of extremes rather than averages. We have addressed this by examining the change in frequency of exceeding a daily mean temperature threshold, defined as ‘disruption days’, as it is often this exceedance which has the most dramatic impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution of climate change both geographically and temporally, the latter specifically to address the 5‐ to 20‐year time horizon which can be recognized in business planning.We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias‐corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst‐case scenario planning.Taking the city of Chicago as an example, the expected frequency of years with 40 or more disruption days above the 25°C threshold rises by a factor of four for a time period centred on 2040, compared with a period centred on 2000. Alternately, looking at the change in the number of days at a given likelihood, an example is Shenzhen, where the number of disruption days in a once‐per‐decade event exceeding the 25°C or 30°C threshold is expected to rise by a factor of four.In a future stage, superimposing these results onto maps of, for instance, GDP sensitivity or production days lost, will provide more accurate and targeted conclusions for future impacts of climate change. This method of quantifying costs on business‐relevant timescales will enable businesses and governments properly include risks associated with facilities, plan mitigating actions and make accurate provisions. It can also, for example, inform their disclosure of physical risks under the framework of the Task Force on Climate‐related Financial Disclosures. This approach is equally applicable to other weather‐related, localized phenomena likely to be impacted by climate change.

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four process-based models). The differentiating factor of the second ensemble was the application of bias correction to the GCM-Ws, using a hindcast dataset as the reference. All ensemble members were weighted according to their performance to reproduce the reference LST patterns for the baseline period. Additionally, the total uncertainty of the LST projections was decomposed into the main sources and their interactions using the ANOVA method. Finally, the robustness of the LST projections was checked. Comparison of the projected changes in LST rates obtained from two ensembles indicated that the bias correction could relatively reduce the ranges of the uncertainty in the LST projections. On the annual scale, the contribution of emission scenarios, GCM-Ws, LST models and non-linear interactions to the total uncertainty was about 10–20, 35–50, 5–15, and 30–35%, respectively. Overall, the weighted means of the ensembles reported a decrease in net annual mean LST rates (less than 10% under RCP 4.5, a 10–20% under RCP 8.5). However, no robust projected changes in LST rates on annual and seasonal scales were found, questioning any ultimate decision being made using the means of the projected changes.

Uncertainty quantification in intensity-duration-frequency curves under climate change: Implications for flood-prone tropical cities

Updating the existing intensity–duration–frequency (IDF) curves (focusing on 1-24 h) to reduce the global warming risks are typically based on regional climate models (RCMs). However, uncertainties are known to be present in such updating and need quantifying, especially whether bias correction (BC) methods should be used or not is less understood. Furthermore, the updating is still challenging for flood-prone cities in Southeast Asia due to a lack of sub-daily gauge-based observations. Therefore, in this study, firstly, we investigate the uncertainties related to the use of the BC, and the choices of different RCMs, RCP scenarios, parameter estimation methods by using the ‘analysis of variance’ method. Secondly, we propose a framework for developing and updating IDF curves in the data-scarce city (with only daily gauge observations) in Cambodia (Phnom Penh City (PPC)) by utilizing the rainfall information from the nearby cities (Ho Chi Minh City (HCMC) and Can Tho City (CTC) in Vietnam), based on the scaling property of rainfall duration versus intensity. A comparison between the RCMs with and without BC reveals that BC can efficiently remove the model bias, and the changes of rainfall intensity with BC are consistent with the underlying physical mechanisms (i.e., where atmospheric circulation tends to drive a larger increase in rarer rainfall events than less rare ones), supporting the application of BC before developing IDF curves. Further quantification of different uncertainty sources reveals that RCMs account for the largest share of the total variance (41.0% and 47.9% in HCMC and CTC, respectively). It is followed by the BC, with 39.4% and 29.7% in the two cities, respectively. RCP scenarios and parameter estimation methods provide the comparable and lowest uncertainty. The projected IDF curves in PPC, developed based on the integrated rainfall information (i.e., daily rainfall in PPC and scaling properties in nearby cities), reveal an apparent upshift (e.g., 33.6–43.7% increase in 20–year rainfall intensity in the near future (2026–2045) under RCP 4.5) of the IDF curves compared with the curves in the historical period (1986–2005). However, the deep uncertainty in such projection requires us to adopt a dynamic adaption strategy that can be iteratively updated based on new information. The current framework on quantifying different uncertainty sources and updating the IDF curves for data-scarce cities can provide suggestions for similar studies in other regions globally.

Bias Correction of Surface Air Temperature and Precipitation in Cordex East Asia Simulation: What Should We Do When Applying Bias Correction?

Bias Correction of Surface Air Temperature and Precipitation in Cordex East Asia Simulation: What Should We Do When Applying Bias Correction?

The impact of data assimilation into the meteorological WRF model on birch pollen modelling

We analyse the impact of ground-based data assimilation to the Weather Research and Forecasting (WRF) meteorological model on parameters relevant for birch pollen emission calculations. Then, we use two different emission databases (BASE – no data assimilation, OBSNUD – data assimilation for the meteorological model) in the chemical transport model and evaluate birch pollen concentrations. Finally, we apply a scaling factor for the emissions (BASE and OBSNUD), based on the ratio between simulated and observed seasonal pollen integral (SPIn) to analyse its impact on birch concentrations over Central Europe. Assimilation of observational data significantly reduces model overestimation of air temperature, which is the main parameter responsible for the start of pollen emission and amount of released pollen. The results also show that a relatively small bias in air temperature from the model can lead to significant differences in heating degree days (HDD) value. This may cause the HDD threshold to be attained several days earlier/later than indicated from observational data which has further impact on the start of pollen emission. Even though the bias for air temperature was reduced for OBSNUD, the model indicates a start for the birch pollen season that is too early compared to observations. The start date of the season was improved at two of the 11 stations in Poland. Data assimilation does not have a significant impact on the season's end or SPIn value. The application of the SPIn factor for the emissions results in a much closer birch pollen concentration level to observations even though the factor does not improve the start or end of the pollen season. The post-processing of modelled meteorological fields, such as the application of bias correction, can be considered as a way to further improve the pollen emission modelling.