Global Climate Model Outputs Research Articles

Temporal and spatial evolutionary trends of regional extreme precipitation under different emission scenarios: Case study of the Jialing River Basin, China

Exploration of the temporal and spatial evolutionary trends of future extreme precipitation under different emission scenarios is of considerable importance for promoting active response to climate change. Based on the output of CMIP6 global climate models (GCMs) with four Shared Socioeconomic Pathways (SSPs), precipitation data for the Jialing River Basin (China) were obtained through CMhyd downscaling and correction. A relative threshold of 95 percentile of each year was used to extract extreme precipitation in the basin, and historical observational data were used to verify the capability of the GCMs to simulate extreme precipitation. Based on Extreme-point Symmetric Mode Decomposition, the trends and periodic characteristics of future extreme precipitation in the Jialing River Basin under four different emission scenarios (SSP2.6, SSP4.5, SSP7.0, and SSP8.5) were simulated and analyzed. The future temporal and spatial distributions of extreme precipitation frequency and intensity in different periods were described. The results revealed the following. (1) Future extreme precipitation in the Jialing River Basin is likely to increase with rates of 21.8 mm/10a, 11.4 mm/10a, 8.7 mm/10a, and 8.3 mm/10a for SSP8.5, SSP 7.0, SSP4.5, and SSP2.6, respectively. (2) The frequency of extreme precipitation decreased in the whole basin with a 1-day decrease of 3.88 % (SSP8.5), 7.11 % (SSP7.0), 5.66 % (SSP4.5), and 2.40 % (SSP2.6) from historical levels. (3) The intensity of extreme precipitation increased with a 1-day increase of 11.18 % (SSP8.5), 6.80 % (SSP7.0), 5.04 % (SSP4.5), and 4.57 % (SSP2.6) from historical levels. Over time, the change of extreme precipitation showed strong periodic characteristics with three periodic components of 2–3, 4–6, and 9–12 years. In terms of spatial distribution, the frequency (intensity) center of extreme precipitation in the next 80 years will be distributed in the south (southeast) of the basin. Compared with historical observations, the area of extreme precipitation intensity in the future accounted for a larger proportion, while the area of extreme precipitation frequency accounted for a smaller proportion. In terms of increased emission levels, the extreme precipitation frequency would decrease and the intensity would increase. The conclusions of this study could provide a reference for disaster prevention and mitigation of the effects of extreme precipitation under future climate change.

Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes, especially in areas with complex terrain. In this study, we proposed a statistical downscaling (SD) model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models (GCMs) for present and future (2081–2100) periods under two shared socioeconomic pathways (SSP245 and SSP585). The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity (SDII) and maximum 1-day precipitation (RX1DAY) and overestimate the number of wet days (R1MM) and maximum consecutive wet days (CWD) at stations across CA. However, the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations. Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA. In addition, it is skilled in capturing the spatial patterns of the observed precipitation indices. Obviously, SDII and RX1DAY are improved by the SD model, especially in the southeastern mountainous area. Under the intermediate scenario (SSP245), our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation (R95PTOT) of 0.5 mm d−1 and 19.7 mm, respectively, over CA at the end of the 21st century (2081–2100) compared to the present values (1995–2014). More pronounced increases in indices R95PTOT, SDII, number of very wet days (R10MM), and RX1DAY are projected under the higher emission scenario (SSP585), particularly in the mountainous southeastern region. The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081–2100. The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

Spatial and Temporal Analysis of Extreme Precipitation under Climate Change over Gandaki Province, Nepal

This paper presents a research study of expected precipitation extremes across the Gandaki Province, Nepal. The study used five indices to assess extreme precipitation under climate change. Precipitation output of two Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase Six (CMIP6) were used to characterize the future precipitation extremes during the rainfall season from June to September (JJAS) and overall days of the year. To characterize extreme precipitation events, we used daily precipitation under the SSP2–4.5 and SSP5–8.5 scenarios from the Beijing Climate Center and China Meteorological Administration, China; and Meteorological Research Institute (MRI), Japan. Considering large uncertainties with GCM outputs and different downscaling (including bias correction) methods, direct use of GCM outputs were made to find change in precipitation pattern for future climate. For 5-, 10-, 20-, 50-, and 100-year return periods, observed and projected 24 h and 72 h annual maximum time series were used to calculate the return level. The result showed an increase in simple daily intensity index (SDII) in the near future (2021–2040) and far future (2081–2100), with respect to the base-year (1995–2014). Similarly, heavy precipitation days (R50 mm), very heavy precipitation days (R100 mm), annual daily maximum precipitation (RX1day), and annual three-day maximum precipitation (RX3day) indices demonstrated an increase in extreme precipitation toward the end of the 21st century. A comparison of R50 mm and R100 mm values showed an extensive (22.6% and 63.8%) increase in extreme precipitation days in the near future and far future. Excessive precipitation was forecasted over Kaski, Nawalparasi East, Syangja, and the western half of the Tanahun region. The expected increase in extreme precipitation may pose a severe threat to the long-term viability of social infrastructure, as well as environmental health. The findings of these studies will provide an opportunity to better understand the origins of severe events and the ability of CMIP6 model outputs to estimate anticipated changes. More research into the underlying physical factors that modulate the occurrence of extreme incidences expected for relevant policies is suggested.

High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand

The gap in resolution between existing global climate model output and that sought by decision-makers drives an ongoing need for climate downscaling. Here we test the extent to which developments in deep learning can out-perform existing statistical approaches for downscaling historical rainfall in the highly complex terrain setting of New Zealand. While deep learning removes the need for manual feature selection when extracting spatial-temporal information from predictor fields, several key considerations need to be addressed. These include: the chosen complexity of the network architecture, suitable loss functions tailored to the problem, as well as input data considerations of domain size and amount of training data required to provide adequate out-of-sample generalization. Sensitivity testing to these considerations reveals that a relatively simple convolutional neural network (CNN) architecture with carefully selected loss functions can considerably outperform existing statistical downscaling models based on multiple linear regression with manual feature selection. When aggregated across the entire region, the fraction of explained variance on wet days increased from 0.35 to 0.52, the root-mean-squared error reduced by over 20% and percentage biases for the 90th percentile of rainfall improved by over 25%. Using interpretable machine learning methods, we demonstrate that the CNN has been capable of self-learning physically plausible relationships between the large-scale atmospheric environment and extreme localized rainfall events. The historical performance and physical interpretability documented here lends support for wider development and application of deep learning in climate downscaling.

Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review.

Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy.

Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks

Abstract Near-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach based on deep learning and a high-resolution digital elevation model to spatially downscale hourly near-surface wind fields at coarse resolution from ERA5 reanalysis from their original 25-km grid to a 1.1-km grid. A 1.1-km-resolution wind dataset for 2016–20 from the operational numerical weather prediction model COSMO-1 of the national weather service MeteoSwiss is used to train and validate our model, a generative adversarial network (GAN) with gradient penalized Wasserstein loss aided by transfer learning. The results are realistic-looking high-resolution historical maps of gridded hourly wind fields over Switzerland and very good and robust predictions of the aggregated wind speed distribution. Regionally averaged image-specific metrics show a clear improvement in prediction relative to ERA5, with skill measures generally better for locations over the flatter Swiss Plateau than for Alpine regions. The downscaled wind fields demonstrate higher-resolution, physically plausible orographic effects, such as ridge acceleration and sheltering, that are not resolved in the original ERA5 fields. Significance Statement Statistical downscaling, which increases the resolution of atmospheric fields, is widely used to refine the outputs of global reanalysis and climate models, most commonly for temperature and precipitation. Near-surface winds are strongly modified by the underlying topography, generating local flow conditions that can be very difficult to estimate. This study develops a deep learning model that uses local topographic information to spatially downscale hourly near-surface winds from their original 25-km resolution to a 1.1-km grid over Switzerland. Our model produces realistic high-resolution gridded wind fields with expected orographic effects but performs better in flatter regions than in mountains. These downscaled fields are useful for impact assessment and decision-making in regions where global reanalysis data at coarse resolution may be the only products available.

Downscaling approaches of climate change projections for watershed modeling: Review of theoretical and practical considerations

Water resources managers must increasingly consider climate change implications of, whether the concern is floods, droughts, reservoir management, or reliably supplying consumers. Hydrologic and water quality modeling of future climate scenarios requires understanding global climate models (GCMs), emission scenarios and downscaling GCM output, since GCMs generate climate predictions at a resolution too coarse for watershed modeling. Here we present theoretical considerations needed to understand the various downscaling methods. Since most watershed modelers will not be performing independent downscaling, given the resource and time requirements needed, we also present a practical workflow for selecting downscaled datasets. Even given the availability of a number of downscaled datasets, a number of decisions are needed regarding downscaling approach (statistical vs. dynamic), GCMs to consider, options, climate statistics to consider for the selection of model(s) that best predict the historical period, and the relative importance of different climate statistics. Available dynamically-downscaled datasets are more limited in GCMs and time periods considered, but the watershed modeler should consider the approach that best matches the historical observations. We critically assess the existing downscaling approaches and then provide practical considerations (which scenarios and GCMs have been downscaled? What are some of the limitations of these databases? What are the steps to selecting a downscaling approach?) Many of these practical questions have not been addressed in previous reviews. While there is no “best approach” that will work for every watershed, having a systematic approach for selecting the multiple options can serve to make an informed and supportable decision.

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.

XCast: A python climate forecasting toolkit

Climate forecasts, both experimental and operational, are often made by calibrating Global Climate Model (GCM) outputs with observed climate variables using statistical and machine learning models. Often, machine learning techniques are applied to gridded data independently at each gridpoint. However, the implementation of these gridpoint-wise operations is a significant barrier to entry to climate data science. Unfortunately, there is a significant disconnect between the Python data science ecosystem and the gridded earth data ecosystem. Traditional Python data science tools are not designed to be used with gridded datasets, like those commonly used in climate forecasting. Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. A set of high-performance, easy-to-use Python climate forecasting tools is needed to bridge the gap between Python's data science ecosystem and its gridded earth data ecosystem. XCast, an Xarray-based climate forecasting Python library developed by the authors, bridges this gap. XCast wraps underlying two-dimensional data science methods, like those of Scikit-Learn, with data structures that allow them to be applied to each gridpoint independently. XCast uses high-performance computing libraries to efficiently parallelize the gridpoint-wise application of data science utilities and make Python's traditional data science toolkits compatible with multidimensional gridded data. XCast also implements a diverse set of climate forecasting tools including traditional statistical methods, state-of-the-art machine learning approaches, preprocessing functionality (regridding, rescaling, smoothing), and postprocessing modules (cross validation, forecast verification, visualization). These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. We also demonstrate a case study where XCast was used to generate experimental real-time deterministic and probabilistic forecasts for South Asian Summer Monsoon Rainfall in 2022 using different machine learning-based multi-model ensembles.

Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes

An accurate and high spatiotemporal resolution precipitation observation dataset is crucial to the studies of climate change and related impact assessments. Great efforts have been made to evaluate the performance of a variety of precipitation products. However, most studies mainly focused on the assessment of precipitation estimation or precipitation occurrence detection, and seldom evaluated the capabilities of monitoring extreme precipitation of the utilized datasets. We conduct a study to quantitatively evaluate eight high-resolution precipitation products, including APHRODITE, CHIRPS, CMFD, CMORPH, IMERG, MSWEP, PERSIANN, and TMPA, against dense rain gauges in terms of precipitation climatology and extremes over China from 2001 to 2015. Results indicate that CMFD is superior to the rest products in terms of estimating precipitation amounts and monitoring precipitation extremes in China. APHRODITE performs best among all products in detecting precipitation occurrences. Precipitation products also exhibit different levels of performance in four subregions with distinct regional features. For both precipitation climatology and extremes, CMFD has the best applicability in the humid region and the Tibetan Plateau, while APHRODITE and MSWEP outperform other products in the wet-to-arid transition and arid regions, respectively. This work could provide a valuable basis for bias correction and downscaling of global climate model outputs. It also has implications for the selection of precipitation products for local studies and the future development of precipitation products in China.

Piecewise‐quantile mapping improves bias correction of global climate model daily precipitation towards preserving quantiles and extremes

AbstractBias correction is a vital technique to correct the climate model outputs for regional studies. Prior bias correction methods such as quantile mapping (QM) may be problematic in correcting extreme values. Herein we proposed a novel bias correction method, that is, the piecewise‐quantile mapping (PQM), by combing piecewise mapping with QM, which implements bias corrections on both extreme and nonextreme data. We compared the PQM method and other six commonly used methods in correcting daily precipitation in the Mekong River Basin (MRB) and the Yangtze River Basin (YRB) from two global climate models (GCMs) (i.e., EC‐Earth3 and MPI‐ESM1‐2‐HR) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results show that the PQM perform better in bias correction pertaining to both the precipitation percentiles and most of the extremes indices. For example, the mean absolute error of R20mm (the number of days when precipitation ≥20 mm) is significantly reduced from 21.64 to 9.03 days and from 10.96 to 5.71 days over MRB and YRB, respectively. In addition, the PQM could capture the spatial distributions of extremes indices reasonably well. The PQM developed in this study provides more accurate bias correction of the GCMs outputs, which will reduce the uncertainty in the subsequent analyses, such as climate change impacts on hydrological and biogeochemical cycles, particularly under extreme conditions.

Intensity-area-duration-based drought analysis under 1.5°C–4.0 °C warming using CMIP6 over a climate hotspot in South Asia

Global-level warming largely influences drought dynamics. Therefore, a robust drought projection is critical to support sustainable development under a future warming climate, especially at the regional level (i.e., South Asia). Encouraged by the Paris Agreement, this study investigates the changes in the intensity, area, and duration of future droughts under 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C global warming levels over South Asia. In this regard, seven global climate model outputs under seven newly designed climate change scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are used. The standardized precipitation evapotranspiration index (SPEI), self-calibrating Palmer drought severity index (scPDSI), and intensity-area-duration (IAD) method are adopted to characterize drought. Drought events are identified considering an area coverage greater than 50,000 km2 for predefined drought durations of 3- (short-term), 6- (medium-term), and 12-month (long-term). A significant drying trend is seen and tested against the two-sample t-test. Notably, to represent the more concrete picture of changes in drought characteristics, the results based on two indices (SPEI and scPDSI) are combined (average) in this paper. The results reveal that droughts with higher-intensity, and long-term duration will increase with continued warming. As drought features, the number of long-term droughts will increase by 21.5% (highest), and the leading area coverage is also estimated (>7%) for long-term drought at higher warming (i.e.4.0 °C) than the reference period (1995–2014). Furthermore, drought events with large-area (>3 × 105 km2) coverage are projected to become more frequent. For drought severity, extreme drought events and associated area coverage are more likely to unfold over the region. However, overall drought conditions are inclined to worsen under higher warming, especially 4.0 °C warming. Importantly, for global warming targets set by the Paris Agreement (1.5 °C and 2.0 °C), drought conditions will be more acute at 2.0 °C than at 1.5 °C. Spatially, a significant drying trend is mainly observed in the western part of South Asia (Southwest-Northwest) according to both drought metrics (SPEI and scPDSI). Notably, NW will experience acute droughts under all warming levels. Droughts in this region will be mainly influenced by the combined effect of decreasing precipitation and escalated evapotranspiration. As global warming increases, a significant increase in PET and temperature is found over South Asia. In addition, the model evaluation shows that the CMIP6-Global climate models (GCMs) are capable of satisfactorily reproducing climate variables and drought events in South Asia. However, our study's findings strongly support the execution of the Paris Agreement target and provide a scientific basis for articulating drought adaptation measures at the regional scale, especially in South Asia.

Climate Change Impacts on Agricultural Water Availability in the Middle Rio Grande Basin

AbstractWe present a comprehensive analysis of water availability under plausible future climate conditions in a heavily irrigated agricultural watershed located in the middle section of the Rio Grande Basin in the United States Desert Southwest. Future managed streamflow scenarios (through year 2099) were selected from among 97 scenarios developed based on downscaled, bias‐corrected global climate model outputs to evaluate future inflows to the principal surface water storage reservoirs, possible future reservoir releases, and groundwater pumping to sustain irrigated agriculture. The streamflow projections describe a wide range of dry and wet conditions compared to the average historical flows in the river, indicating significant uncertainty in future water availability in the Rio Grande Basin. We applied the Soil and Water Assessment Tool to illustrate the impact of climate futures on different components of the water budget at a watershed scale. Results indicate declining reliability of reservoir storage to meet the water demand of irrigated agriculture. The impact of declining surface water can be offset by increasing the pressure on the already‐strained groundwater resources. However, the region should be prepared to use slightly saline (total dissolved solids [TDS] > 1,000 mg/L) and moderately saline groundwater (TDS > 3,000 mg/L) as fresh groundwater in the regional aquifer is depleted within the 21st Century under hotter and drier conditions and status quo agricultural land and water management practices.

Automatic variable selection in ecological niche modeling: A case study using Cassin's Sparrow (Peucaea cassinii).

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

Towards better characterization of global warming impacts in the environment through climate classifications with improved global models

AbstractClimate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%).

Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios

Assessment of climate change impacts on wind characteristics is crucial for the design, operation, and maintenance of coastal and offshore infrastructures. In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU‐ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%.

Alternative Implementations of the “Pseudo‐Global‐Warming” Methodology for Event‐Based Simulations

AbstractThe pseudo‐global‐warming (PGW) methodology provides an efficient means to investigate the response of a weather or climate event under an imposed climate change signal. In the traditional PGW implementation, this signal is represented through climate‐change “deltas” constructed using monthly averages of global climate model (GCM) output over decadal or longer periods during the past and future. The implications of alternative formulations of such deltas were explored herein. Diurnally varying (DV) deltas were compared to the time‐constant (TC) deltas used in the traditional PGW implementation; this was done to test the potential effect of future changes in the diurnal cycles of temperature, humidity, and winds. Deltas created using 10‐year averages were compared to those using 30‐year averages, to examine the effects of the time‐averaging period in the delta construction. Finally, the common practice of additionally averaging across multiple GCMs to form a composite delta was also considered. Using simulations of three different historical convective storm events, it was shown that each of these PGW delta formulations results in differences in simulation metrics such as total accumulated rainfall, and convective intensity, but major and/or unambiguous differences were not always found. It is recommended that users of the PGW approach carefully consider all implications of delta formulation on their particular problem.

Projecting Hydrological Responses to Climate Change Using CMIP6 Climate Scenarios for the Upper Huai River Basin, China

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Changes in extreme precipitation across South Asia for each 0.5 °C of warming from 1.5 °C to 3.0°C above pre-industrial levels

Motivated by the Paris Agreement, this study aims to investigate the changes in precipitation extremes across South Asia and its five climatic zones for each 0.5 °C of warming above the pre-industrial level from 1.5 °C to 3.0 °C. In this regard, 20 global climate model outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) are used. Four widely used extreme precipitation indices such as number of consecutive wet days (CWD), number of heavy precipitation days (R10mm), maximum consecutive 5-day precipitation (RX5day), and the number of very wet days (R99pTOT) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are applied to detect the extreme events. To ensure reduced uncertainty, we used bias-corrected and multi-model ensemble outputs. Results indicate that as the degree of global warming increases, the changes in magnitudes of extreme events are projected to intensify (except for CWD) and the largest growth is found under 3.0 °C in the entire domain and its five climatic zones. The polar climatic zone is anticipated to experience the highest magnitude of changes for extreme events under all the warming scenarios. The largest percentage of area with significant increase is found under the highest warming level (3.0 °C), especially for R95pTOT (95.5% of the entire domain). For an additional 0.5 °C (2.0–1.5 °C) of warming, the anticipated precipitation-related extremes will increase by 3.5%, (RX5day), 3.6% (R10mm) and 6.6% (R99pTOT), as mitigation targets set out in the Paris Agreement. The projected intense sea surface temperature over the Arabian Sea, and large water vapor content over South Asian landmass are expected to influence precipitation-related extreme events. Notably, considering all aspects of our analysis, R99pTOT is expected to be the most predominant extreme precipitation index across the domain with continued warming. So, our findings strongly support the Paris Agreement target to limit global warming to 1.5 °C and provide a scientific basis.