General Circulation Model Output Research Articles

Intercomparison of machine learning methods for statistical downscaling of daily temperature under CMIP6 scenarios: a case study from Iran

ABSTRACT General circulation models (GCM) represent a contemporary and advanced tool designed to simulate the response of climate system to alterations in greenhouse gas levels. Increasing spatial resolutions of the outputs of GCMs on a regional scale requires downscaling process. In this study, four machine learning (ML) models, namely, decision tree regression (DTR), support vector regression, artificial neural network, and K-nearest neighbors (KNN) were employed to downscale the outputs of four Coupled Model Intercomparison Project 6 climate models. The daily observations of temperature data at Nazmakan station in Kohgiluyeh and Boyer-Ahmad Province, Iran, were trained and tested for the periods 1995–2009 and 2009–2015, respectively. According to results of four metrics, DTR and KNN obtained the most valid evaluation in the training and test data, respectively. In addition, downscaling methods were used to predict future daily temperature for 2015–2045 under two Shared Socioeconomic Pathway (SSP2-4.5 and SSP5-8.5) scenarios. Mann–Kendall test results revealed an increasing trend of temperature in most of the coming years. The projected trend expects cooler summers and warmer winters in future. Furthermore, the predicted data under two climate scenarios were assessed in comparison with observed data for 2015–2022. Finally, this study demonstrates the strengths and weaknesses of nonlinear ML techniques in statistical downscaling.

A copula post-processing method for wind power projections under climate change

Wind energy plays a pivotal role in the ongoing effort to reduce carbon emissions in the energy sector. With the increasing evidence of climate change, there is a growing concern regarding the planning and operation of wind energy resources. Accurate forecasts are essential to understand the frequency distribution of wind speed data in a given area and, consequently, to estimate energy production. This paper aims to analyze the wind resources under climate change, assess their potential, and create zoning maps for wind energy production in the island of Ireland. For this objective, wind speed data from 31 general circulation models (GCMs) and two climate change scenarios were utilized for both hindcast and forecast periods in 1981–2010 and 2021–2050, respectively. The GCM outputs were first bias-corrected and then post-processed using various (non–)parametric statistical distributions and 3 Copula families. The results indicate an expected decrease in the average wind speed in the region up to ∼ 21 % by 2050, contingent on the climate scenarios under consideration and the target point. Ultimately, this study concludes by presenting wind power density maps specifically to the study region, offering valuable insights for sustainable energy planning.

Climate variability impacts on runoff projection in the 21st century based on the applicability assessment of multiple GCMs: A case study of the Lushi Basin, China

In this study, we initiatively employed the improved Secure Hash Algorithm (SHA) to evaluate and compare the competencies of multiple Coupled Model Intercomparison Project Phase Six (CMIP6) General Circulation Models (GCMs). The selected GCM outputs were bias-corrected by the non-parametric quantile mapping (QM) method. The calibrated and validated data were then used to run the XAJ (Xin’anjiang) model to assess long-term climate variation impacts on runoff under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), where the results were compared with the baseline period (1976–2000). Overall, the CNRM-CM6-1-HR, FIO-ESM-2–0, INM-CM5-0 and NorESM2-MM models reconstructed better spatio-temporal evolutionary trends related to historical precipitation and evaporation, and were applied as selected GCMs for reliable runoff projection applications. The intensification amplitudes of precipitation and evaporation produced by selected GCMs gradually spread from SSP1-2.6 to SSP5-8.5 throughout the 21st century. Compared to the baseline period (257 m3/s), the extreme streamflow during the wet periods is likely to increase for SSP5-8.5 (288 ∼ 298 m3/s), SSP3-7.0 (276 ∼ 294 m3/s), and SSP2-4.5 (222 ∼ 280 m3/s), whereas SSP1-2.6 decreases to varying degrees (178 ∼ 255 m3/s). Meanwhile, during the dry periods, the extreme streamflow is projected to decrease by 28 ∼ 73 m3/s (SSP5-8.5), 32 ∼ 38 m3/s (SSP3-7.0), 8 ∼ 31 m3/s (SSP2-4.5), and 5 ∼ 33 m3/s (SSP1-2.6) from historical levels (62 m3/s). The overwhelming majority of increasing trends in the wet periods and vastly decreasing trends in the dry seasons indicate that the basin will face some floods and water shortage risks in the mid to late 21st century. The findings broaden the efficacious applications of SHA in the performance selection of GCMs and QM method in bias correction. The conclusions provide a reference for flood and drought mitigation planning and water resources policymaking in the Yellow River Basin.

Firn air content changes on Antarctic ice shelves under three future warming scenarios

Abstract. The Antarctic firn layer provides pore space in which an estimated 94 % to 96 % of the surface melt refreezes or is retained as liquid water. Future depletion of firn pore space by increased surface melt, densification and formation of low-permeability ice slabs can potentially lead to meltwater ponding, hydrofracturing and ice-shelf disintegration. Here, we investigate the 21st-century evolution of total firn air content (FAC) and accessible FAC (i.e. the pore space that meltwater can reach) across Antarctic ice shelves. We use the semi-empirical IMAU Firn Densification Model (IMAU-FDM) with an updated dynamical densification expression to cope with changing climate forcing. The firn model is forced by general circulation model output of the Community Earth System Model version 2 (CESM2) for three climate emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5), dynamically downscaled to a 27 km horizontal resolution by the Regional Atmospheric Climate Model version 2.3p2 (RACMO2.3p2). To estimate accessible FAC, we prescribe a relationship between ice-slab thickness and permeability. In our simulations, ice shelves on the Antarctic Peninsula and the Roi Baudouin Ice Shelf in Dronning Maud Land are particularly vulnerable to total FAC depletion (> 50 % decrease by 2100), even for low-emission (SSP1-2.6) and intermediate-emission (SSP2-4.5) scenarios. In the high-emission (SSP5-8.5) scenario in particular, the formation of ice slabs further reduces accessible FAC on ice shelves with low accumulation rates (current rates of < 500 mmw.e.yr-1), including many East Antarctic ice shelves and the Filchner–Ronne, Ross, Pine Island and Larsen C ice shelves. These results underline the potentially large vulnerability of low-accumulation ice shelves to firn air depletion through ice-slab formation.

Assessment of Climate Change Impact on Reservoir Inflow

Abstract: Evaluating the effects of climate change is crucial in regions grappling with water scarcity. The Ujjani dam, a significant structure in Maharashtra, India, erected on the Bhima river in 1980, serves as a vital water source for the agricultural lands downstream in Solapur and Pune districts. Assessing climate change's impact involves analysing data from General Circulation Models (GCMs), which project climate parameters under various emission scenarios but often at a broader scale. Since hydrological models necessitate finer-scale climate data, a downscaling technique is employed to derive localized variables from GCM outputs. This study employs Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques for downscaling. By utilizing projected temperature and rainfall data, three distinct models are developed to predict reservoir inflow: multiple linear regression, artificial neural network, and wavelet neural network. The findings indicate significant alterations in rainfall distribution patterns, with a decline during the monsoon season and an increase post-monsoon

Process‐Based Intercomparison of Water Isotope‐Enabled Models and Reanalysis Nudging Effects

AbstractThe products from the Stable Water Isotope Intercomparison Group, Phase 2, are currently used for numerous studies, allowing water isotope model‐data comparisons with various isotope‐enabled atmospheric general circulation model (AGCMs) outputs. However, the simulations under this framework were performed using different parameterizations and forcings. Therefore, a uniform experimental design with state‐of‐the‐art AGCMs is required to interpret isotope observations rigorously. Here, we evaluate the outputs from three isotope‐enabled numerical models nudged by three different reanalysis products and investigate the ability of the isotope‐enabled AGCMs to reproduce the spatial and temporal patterns of water isotopic composition observed at the surface and in the atmospheric airborne water. Through correlation analyses at various spatial and temporal scales, we found that the model's performance depends on the model or reanalysis we use, the observations we compare, and the vertical levels we select. Moreover, we employed the stable isotope mass balance method to conduct decomposition analyses on the ratio of isotopic changes in the atmosphere. Our goal was to elucidate the spread in simulated atmospheric column δ18O, which is influenced by factors such as evaporation, precipitation, and horizontal moisture flux. Satisfying the law of conservation of water isotopes, this budget method is expected to explain various fractionation phenomena in atmospheric meteorological and climatic events. It also aims to highlight the spreads in modeled isotope results among different experiments using multiple models and reanalyses, which are primarily dominated by uncertainties in moisture flux and precipitation, respectively.

Unveiling an Environmental Drought Index and its applicability in the perspective of drought recognition amidst climate change

As a complex natural disaster, drought encompasses significant and wide-ranging impacts on various environmental aspects. While meteorological, hydrological, agricultural, and socioeconomic droughts have been extensively studied, the scientific understanding of environmental droughts (the proposed fifth classification) remains relatively limited, hampering practical assessment efforts. To address this gap, the present study, for the first time, conducted a rigorous assessment of the applicability of a novel method, namely the heuristic method, in conjunction with a newly developed Environmental Drought Index (EDI). The present study thoroughly analyzed environmental drought events in India's Brahmani River basin, specifically focusing on the Jaraikela catchment. Firstly, the Minimum in-stream Flow Requirement (MFR) was determined using Tennant’s method to synthetically estimate discharge rates to maintain the optimum flow range during the historical period (1980–2014). Secondly, Drought Duration Length (DDL) was calculated by counting consecutive water deficit months with negative monthly Streamflow Rate (SFR) and MFR differences. Three General Circulation Models (GCMs) output ensembles, namely EC-Earth3, MPI-ESM1-2-HR, and MRI-ESM2-0, participating in CMIP-6, were used for past (1980–2014) and future periods (FP-1: 2015–2022, FP-2: 2023–2045) under emission scenarios SSP245 and SSP585. The HydroClimatic Conceptual Streamflow (HCCS) model was employed to simulate the historical and future SFR. Thirdly, the largest water deficit magnitude during DDL was used to estimate the Water Shortage Level (WSL). Finally, integrating DDL and WSL provided the EDI for each environmental drought event. Results demonstrated a strong correspondence between the simulated EDI obtained using MPI-ESM1-2-HR under SSP585 and the observed EDI values, thereby indicating the credibility of the EDI in assessing environmental droughts. Furthermore, the study found severe droughts (i.e., EDI-3) dominating (71–73% of all droughts; occurring during non-monsoonal months) during FP-2 under SSP585 across all three GCMs, differing from moderate droughts in SSP245 of FP-2, both scenarios of FP-1, and the historical period. Based on the findings, the study finally proposed several adaptive measures to mitigate the impacts of increasing environmental drought events in the catchment.

Calibrated multi‐model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

AbstractBangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate‐risk‐prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi‐model ensemble (CMME) of seven state‐of‐the‐art general circulation models (GCMs) from the North American Multi‐Model Ensemble project. The lead‐1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA‐based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real‐time seasonal rainfall forecasts using this new forecast system.

Future changes in the mean and variability of extreme rainfall indices over the Guinea coast and role of the Atlantic equatorial mode

Abstract. The occurrence of climate extremes could have dramatic impacts on various sectors such as agriculture, water supply, and energy production. This study aims to understand part of the variability in the extreme rainfall indices over Guinea coast that can be related to the Atlantic equatorial mode (AEM), whose positive phases are associated with an increase in the intensity and frequency of rainfall events. We use six extreme indices computed from six observed rainfall databases and historical and SSP5-8.5 simulations from 24 general circulation models (GCMs) that participate in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to study changes in extreme rainfall events over Guinea coast during July–September. Under present-day conditions, we found that current GCMs clearly overestimate the frequency of wet events and the maximum number of consecutive wet days. The magnitude of the other extreme indices simulated is within the range of the observations which, moreover, present a large spread. Our results confirm the existing studies. However, less attention has been paid to the evaluation of the modelled rainfall extremes associated with the AEM under different climate conditions, while the variability of the AEM is expected to decrease in the future, with a potentially significant impact on the extreme events. Here, we use six (one) observed rainfall (sea surface temperature) data and 24 GCM outputs to investigate the present-day, near-term, mid-term, and long-term future links between the AEM and the extreme rainfall events over the Guinea coast. The biases in the extreme rainfall responses to the AEM are subject to a large spread across the different models and observations. For the long-term future (2080–2099), less frequent and more intense rainfall events are projected. As an illustration, the multimodel ensemble median (EnsMedian) maximum rainfall during 5 consecutive wet days (RX5day) would be 21 % higher than under present-day conditions. Moreover, the variability of the majority of the extreme indices over the Guinea coast is projected to increase (48 % for RX5day in the long-term future). By contrast, the decreased variability of the AEM in a warmer climate leads to a reduced magnitude of the rainfall extreme responses associated with AEM over the Guinea coast. While under present-day conditions the AEM explains 18 % of the RX5day variance in the EnsMedian, this value is reduced to 8 % at the end of 21st century. As a consequence, in absolute, there is a projected increase in the total variability of most of the extreme rainfall indices, but the contribution of the AEM to this variability weakens in a warmer future climate.

Dynamically coupled kinetic chemistry in brown dwarf atmospheres – I. Performing global scale kinetic modelling

ABSTRACT The atmospheres of brown dwarfs have been long observed to exhibit a multitude of non-equilibrium chemical signatures and spectral variability across the L, T, and Y spectral types. We aim to investigate the link between the large-scale 3D atmospheric dynamics and time-dependent chemistry in the brown dwarf regime, and to assess its impact on spectral variability. We couple the miniature kinetic chemistry module ‘mini-chem’ to the Exo-FMS general circulation model (GCM). We then perform a series of idealized brown dwarf regime atmospheric models to investigate the dynamical 3D chemical structures produced by our simulations. The GCM output is post-processed using a 3D radiative transfer model to investigate hemisphere-dependent spectral signatures and rotational variability. Our results show the expected strong non-equilibrium chemical behaviour brought on by vertical mixing and global spatial variations due to zonal flows. Chemical species are generally globally homogenized, showing variations of ±10 per cent or less, dependent on pressure level, and follow the dynamical structures present in the atmosphere. However, we find localized storm regions and eddies can show higher contrasts, up to ±100 per cent, in mixing ratio compared to the background global mean. This initial study represents another step in understanding the connection between 3D atmospheric flows in brown dwarfs and their rich chemical inventories.

A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level.

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition

Abstract. Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

Evaluating uncertainty in climate change impacts on peak discharge and flood volume in the Qaran Talar watershed, Iran

Abstract This study evaluates the effects of climate change (CC) on runoff properties over the case study of the Qaran Talar watershed located in Iran. To consider the two main sources of uncertainty, i.e., Greenhouse Gases emission (GHG) scenarios and outputs of Atmosphere-Ocean General Circulation Models (AOGCMs), a daily rainfall time series was generated for two future periods (2021-2050 and 2070-2099) at three risk levels of 0.1, 0.25 and 0.50. 22 AOGCMs outputs following two emission scenarios (RCP4.5 and RCP8.5) were used. The results showed that the uncertainty of climate change scenarios was primarily owing to the uncertainty of GCMs outputs. Regarding the 2021-2050 period, under both emission scenarios, the increases in peak discharge and flood volume (FV) were estimated to reach 70, 50, and 30% at three risk levels of 0.1, 0.25 and 0.50, respectively, compared to the recent past period. As the current century draws to a close, the difference between the results of the two emission scenarios becomes apparent so that in the far-future period (2070-2099), the RCP8.5 scenario estimates and FV more than the RCP4.5 scenario does. Additionally, it was found that the uncertainty caused by AOGCMs was more than that by GHG emission scenarios.

Three Regimes of Internal Gravity Wave–Stable Vortex Interaction Classified by a Nondimensional Parameter δ: Scattering, Wheel-Trapping, and Spiral-Trapping with Vortex Deformation

Abstract The internal wave–vortex interaction was investigated for a broad parameter range except near inertial waves, by 1) scaling, 2) numerical experiments, and 3) the estimation of possible occurrences. By scaling, we identified a nondimensional parameter, δ = (V/c)[1/(kR)], where V is the vortex flow speed, R is the radius, c is the incident wave phase speed, and k is the horizontal wavenumber. As δ appears in all terms related to the interaction, it is important in the classification of the wave–vortex interaction. Numerical experiments were conducted on internal waves incident on a stable barotropic vortex with a parameter range of δ = [0.001, 1.7], which is much broader than that used in previous studies (δ ≪ 1). We found new phenomena for δ > 0.15, in addition to previously known scattering for δ ≤ 0.15 (scattering regime). For 0.15 < δ ≤ 0.4, part of the incident internal wave is trapped in a vortex, forming a wheel-like shape maintaining a superinertial frequency (wheel-trapping regime). When δ > 0.4, incident waves are trapped, but with a spiral shape (spiral-trapping regime). Spiral-shaped trapped waves release momentum by wave breaking, which deforms the vortex into a zigzag shape in the vertical direction. Vortex deformation produces vertical shear, which rapidly increases the vertical wavenumber of the incident wave. The distribution of δ in the Pacific Ocean was estimated using a high-resolution (1/30°) ocean general circulation model output. We found the occurrences of all three regimes. The scattering and wheel-trapping regimes are distributed broadly and varied seasonally, thus affecting mixing variability. Significance Statement Oceanic internal waves constitute the fundamental forcing of overturning and material circulation, because internal waves eventually break and cause vertical mixing. Interactions between internal waves and vortices affect wave properties and, therefore, mixing. However, as far as we are aware, all previous studies have focused on large weak vortices relative to waves. Here, we investigated such interactions for a much larger parameter space and identified two new regimes, in which vertical mixing is caused by newly found internal wave trapping and vortex deformation processes. We identified a nondimensional parameter that classifies the regimes and estimated their spatiotemporal distribution. These results suggest new energy routes from internal waves to turbulence and are applicable to other types of waves and vortices.

A Machine Learning Approach for Probabilistic Multi-Model Ensemble Predictions of Indian Summer Monsoon Rainfall

Due to the uncertainty associated with Indian summer monsoon rainfall (ISMR), probabilistic seasonal forecasts which can convey the inherent uncertainty of ISMR are more useful to the user community than a single deterministic forecast. While such probabilistic seasonal forecasts can be produced from general circulation model (GCM) output, one single model generally does not represent all sources of error. The probabilistic multi model ensemble (PMME) is a well-accepted way to improve on the skill of probabilistic forecasts by individual GCMs. PMME can be constructed with one of two approaches: non-parametric, or parametric with respect to the occurrence of three categories of seasonal total rainfall—below, near, and above normal as defined by the climatological base period. Both the methods have their limitations. Non-parametric PMME use a smaller ensemble size which results in overconfident forecasts, and parametric PMME make the inaccurate assumption that total rainfall follows a Gaussian distribution. To avoid these problems, we propose the use of the Extreme Learning Machine (ELM), a novel machine learning (ML) approach, to construct PMME for ISMR forecasting. ELM is a state-of-the-art generalized form of single-hidden-layer feed-forward neural network. However, since the traditional ELM network only produces a deterministic outcome, we use a modified version of ELM called Probabilistic Output Extreme Learning Machine (PO-ELM). PO-ELM uses sigmoid additive neurons and slightly different linear programming to make probabilistic predictions. The performance of such PO-ELM based PMME is assessed rigorously in terms of Generalized Receiver Operating Characteristic scores and reliability diagrams over a 37 years period spanning from 1982 to 2018 following a leave-three-year-out cross-validation scheme. It is demonstrated that our new strategy for PMME based on ML is capable of producing skillful MME forecasts over large regions of India.

Correction to: The climate and compositional variation of the highly eccentric planet HD 80606 b – the rise and fall of carbon monoxide and elemental sulfur

The gas giant HD 80606 b has a highly eccentric orbit (e $\sim$ 0.93). The variation due to the rapid shift of stellar irradiation provides a unique opportunity to probe the physical and chemical timescales and to study the interplay between climate dynamics and atmospheric chemistry. In this work, we present integrated models to study the atmospheric responses and the underlying physical and chemical mechanisms of HD 80606 b. We first run three-dimensional general circulation models (GCMs) to establish the atmospheric thermal and dynamical structures for different atmospheric metallicities and internal heat. Based on the GCM output, we then adopted a 1D time-dependent photochemical model to investigate the compositional variation along the eccentric orbit. The transition of the circulation patterns of HD 80606 b matched the dynamics regimes in previous works. Our photochemical models show that efficient vertical mixing leads to deep quench levels of the major carbon and nitrogen species and the quenching behavior does not change throughout the eccentric orbit. Instead, photolysis is the main driver of the time-dependent chemistry. While CH$_4$ dominates over CO through most of the orbits, a transient state of [CO]/[CH$_4$}] $>$ 1 after periastron is confirmed for all metallicity and internal heat cases. The upcoming JWST Cycle 1 GO program will be able to track this real-time CH$_4$--CO conversion and infer the chemical timescale. Furthermore, sulfur species initiated by sudden heating and photochemical forcing exhibit both short-term and long-term cycles, opening an interesting avenue for detecting sulfur on exoplanets.

Global Asymmetries in the Influence of ENSO on Flood Risk Based on 1,600 Years of Hybrid Simulations

AbstractEl Niño‐Southern Oscillation (ENSO) is often considered as a source of long‐term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to 7 years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1,600 years of bias‐corrected General Circulation Model outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all‐year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk.

Uncertainties in estimating the effect of climate change on 100-year return value for significant wave height

The process of estimating the effect of a changing climate on the severity of future ocean storms is plagued by large uncertainties; for safe design and operation of offshore structures, it is nevertheless important that best possible estimates of climate effects is made given the available data. We explore the variability in estimates of 100-year return value of significant wave height (HS), and changes in estimates over a period of time, for output of WAVEWATCH-III models from 7 representative Coupled Model Intercomparison Project (CMIP) Phase 5 General Circulation Models (GCMs), and the FIO-ESM v2.0 CMIP Phase 6 GCM. Non-stationary extreme value analysis of peaks-over-threshold and block maxima using Bayesian inference provide posterior estimates of return values as a function of time; MATLAB software for the extreme value analysis is provided. Best overall estimates for return values, and changes in return value over the period 1979-2100, are calculated by averaging estimates for individual GCMs. We focus attention on neighbourhoods of locations east of Madagascar and south of Australia where a previous study of CMIP5-derived output reported significant decrease and increase in HS respectively, under Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. There is large variation between return value estimates from different GCMs, and with longitude and latitude within each neighbourhood for estimates based on samples corresponding to ≤ 165 years of model output; these sources of uncertainty tend to be larger than those due to typical modelling choices (such as choice of threshold for peaks over threshold, or block length for block maxima). However, we also find that careful threshold choice and block length are critical east of Madagascar, because of the presence of a mixed population of storms there. Nevertheless, there is general evidence supporting the trends reported by others, but these findings are conditional on the choice of 8 GCMs being representative of climate evolution. We use simple randomisation testing to identify “significant” departures from steady climate. The long 700-year pre-industrial control (piControl) output of the CMIP6 GCM offers an excellent opportunity to quantify the apparent inherent variability in return value as a function of time, estimated using a subsample of output corresponding to a continuous time interval of between 20 and 160 years in length, where no climate forcing is present. We find large variation in return value estimates of approximately ±15% made from samples corresponding to periods of time of around 50 years drawn from piControl data.

Modeling general circulation model bias via a combination of localized regression and quantile mapping methods

Abstract. General circulation model (GCM) outputs are a primary source of information for climate change impact assessments. However, raw GCM data rarely are used directly for regional-scale impact assessments as they frequently contain systematic error or bias. In this article, we propose a novel extension to standard quantile mapping that allows for a continuous seasonal change in bias magnitude using localized regression. Our primary goal is to examine the efficacy of this tool in the context of larger statistical downscaling efforts on the tropical island of Puerto Rico, where localized downscaling can be particularly challenging. Along the way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete climate data network spanning Puerto Rico. Next, we apply a combination of multivariate downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5. Finally, our bias-correction methods are applied to these downscaled GCM climate projections. These bias-correction methods allow GCM bias to vary as a function of a user-defined season (here, Julian day). Bias is estimated using a continuous curve rather than a moving window or monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry season will have increased rainfall, while the early and late rainfall seasons will likely have a decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season (August to November).