Bias correction of global climate model using machine learning algorithms to determine meteorological variables in different tropical climates of Indonesia

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

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Is time a variable like the others in multivariate statistical downscaling and bias correction?

Earth System Dynamics ◽

10.5194/esd-12-1253-2021 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1253-1273

Author(s):

Yoann Robin ◽

Mathieu Vrac

Keyword(s):

Statistical Downscaling ◽

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Synthetic Data ◽

The Other ◽

Strong Increase ◽

Temporal Properties

Abstract. Bias correction and statistical downscaling are now regularly applied to climate simulations to make then more usable for impact models and studies. Over the last few years, various methods were developed to account for multivariate – inter-site or inter-variable – properties in addition to more usual univariate ones. Among such methods, temporal properties are either neglected or specifically accounted for, i.e. differently from the other properties. In this study, we propose a new multivariate approach called “time-shifted multivariate bias correction” (TSMBC), which aims to correct the temporal dependency in addition to the other marginal and multivariate aspects. TSMBC relies on considering the initial variables at various times (i.e. lags) as additional variables to be corrected. Hence, temporal dependencies (e.g. auto-correlations) to be corrected are viewed as inter-variable dependencies to be adjusted and an existing multivariate bias correction (MBC) method can then be used to answer this need. This approach is first applied and evaluated on synthetic data from a vector auto-regressive (VAR) process. In a second evaluation, we work in a “perfect model” context where a regional climate model (RCM) plays the role of the (pseudo-)observations, and where its forcing global climate model (GCM) is the model to be downscaled or bias corrected. For both evaluations, the results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted. However, increasing the number of lags too much does not necessarily improve the temporal properties, and an overly strong increase in the number of dimensions of the dataset to be corrected can even imply some potential instability in the adjusted and/or downscaled results, calling for a reasoned use of this approach for large datasets.

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Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

Journal of Climate ◽

10.1175/jcli-d-14-00754.1 ◽

2015 ◽

Vol 28 (17) ◽

pp. 6938-6959 ◽

Cited By ~ 261

Author(s):

Alex J. Cannon ◽

Stephen R. Sobie ◽

Trevor Q. Murdock

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Coupled Model ◽

Precipitation Extremes ◽

Extreme Value Analysis ◽

Quantile Mapping ◽

Value Analysis

Abstract Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

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High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1785-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1785-1808 ◽

Cited By ~ 29

Author(s):

Lamprini V. Papadimitriou ◽

Aristeidis G. Koutroulis ◽

Manolis G. Grillakis ◽

Ioannis K. Tsanis

Keyword(s):

Global Warming ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Data Set ◽

Low Flows ◽

The Impact ◽

Selection Of

Abstract. Climate models project a much more substantial warming than the 2 °C target under the more probable emission scenarios, making higher-end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under +4 °C of global warming for the European region. Five major European catchments were analysed in terms of future drought climatology and the impact of +2 °C versus +4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce, leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the +2 to the +4° of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational data set for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the Global Climate Model (GCM).

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Bias in downscaled rainfall characteristics

10.5194/hess-2019-139 ◽

2019 ◽

Author(s):

Nicholas J. Potter ◽

Francis H. S. Chiew ◽

Stephen P. Charles ◽

Guobin Fu ◽

Hongxing Zheng ◽

...

Keyword(s):

Water Resource ◽

Bias Correction ◽

Climate Model ◽

Dynamical Downscaling ◽

Transition Probabilities ◽

Global Climate ◽

Global Climate Model ◽

Hydrological Modelling ◽

Quantile Mapping ◽

Rainfall Characteristics

Abstract. Dynamical downscaling of future projections of global climate model outputs can potentially provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand for regional water resource planning processes. By explicitly modelling climate processes within and across global climate model gridcells for a region, dynamical downscaling can provide higher resolution hydroclimate projections, as well as independent (from historical timeseries) and physically plausible future rainfall timeseries for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile-quantile mapping to rainfall dynamically downscaled by NARCliM in the State of Victoria, Australia and examine the effect of this on: (i) biases both before and after bias correction in different rainfall metrics; (ii) change signals in metrics in comparison to the bias; and (iii) the effect of bias correction on wet-wet and dry-dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile-quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. This demonstrates that any quantile mapping bias correction methods are unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias correct dynamical downscaling rainfall outputs.

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Bias Correction in Typhoon and Storm Surge Projection Considering Characteristics of Global Climate Model MRI-AGCM3.2S

Journal of Disaster Research ◽

10.20965/jdr.2015.p0448 ◽

2015 ◽

Vol 10 (3) ◽

pp. 448-456 ◽

Cited By ~ 2

Author(s):

Noriaki Hashimoto ◽

◽

Yukihiro Kinashi ◽

Tomoko Kawashima ◽

Masaki Yokota ◽

...

Keyword(s):

Storm Surge ◽

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Correction Method ◽

Ariake Sea ◽

Design Storm ◽

Meteorological Research Institute ◽

Current Design

The typhoons that so often rage across Japan’s southwestern island, Kyushu, are expected to occur even oftener in the future due to global warming. Storm surge projections have been reported based on the super-high-resolution global climate model MRI-AGCM3.2S developed by Japan’s Meteorological Research Institute (MRI). AGCM3.2S overestimates typhoon strength around Japanese islands, however, and this could lead to exaggerated storm surge projection. We therefore evaluate a bias correction method of typhoon strength considering the typhoon characteristics of AGCM3.2 in estimating maximum storm surge anomaly on the Ariake Sea coast. Our results indicated the possibility of storm surge anomaly of 2.8 m, exceeding the current design storm surge anomaly of 2.36 m at the innermost Ariake Sea.

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Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2019.107858 ◽

2020 ◽

Vol 281 ◽

pp. 107858 ◽

Cited By ~ 1

Author(s):

Ju-Young Shin ◽

Kyu Rang Kim ◽

Jong-Chul Ha

Keyword(s):

Machine Learning ◽

Climate Model ◽

Learning Algorithm ◽

Global Climate ◽

Global Climate Model ◽

Seasonal Forecasting ◽

Machine Learning Algorithm ◽

Field Scale ◽

Air Temperatures ◽

Coupled Global Climate Model

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Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3309-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3309-3314 ◽

Cited By ~ 179

Author(s):

B. Thrasher ◽

E. P. Maurer ◽

C. McKellar ◽

P. B. Duffy

Keyword(s):

Bias Correction ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Strong Relationship ◽

Technical Note ◽

Daily Temperature ◽

Daily Maximum ◽

Temperature Extremes ◽

Quantile Mapping

Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

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The effect of GCM biases on global runoff simulations of a land surface model

10.5194/hess-2017-208 ◽

2017 ◽

Cited By ~ 2

Author(s):

Lamprini V. Papadimitriou ◽

Aristeidis G. Koutroulis ◽

Manolis G. Grillakis ◽

Ioannis K. Tsanis

Keyword(s):

Bias Correction ◽

Land Surface ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Land Surface Model ◽

Global Scale ◽

Surface Model ◽

Precipitation And Temperature ◽

Gcm Biases

Abstract. Global Climate Model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However most state-of-art hydrological models require more forcing variables, additionally to precipitation and temperature, such as radiation, humidity, air pressure and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the land surface model JULES set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four Effect Categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

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