Global Climate Model Simulations Research Articles

Few shot learning for Korean winter temperature forecasts

To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.

Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.

HiCPC: A new 10-km CMIP6 downscaled daily climate projections over China

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China’s specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China.

Enhanced generation of internal tides under global warming

A primary driver of deep-ocean mixing is breaking of internal tides generated via interactions of barotropic tides with topography. It is important to understand how the energy conversion from barotropic to internal tides responds to global warming. Here we address this question by applying a linear model of internal tide generation to coupled global climate model simulations under a high carbon emission scenario. The energy conversion to high-mode internal tides is projected to rise by about 8% by the end of the 21st century, whereas the energy conversion to low-mode internal tides remains nearly unchanged. The intensified near-bottom stratification under global warming increases energy conversion into both low and high-mode internal tides. In contrast, the intensified depth-averaged stratification reduces the modal horizontal wavenumber of internal tides, leading to increased (decreased) energy conversion into high (low)- mode internal tides. Our findings imply stronger mixing over rough topography under global warming, which should be properly parameterized in climate models for more accurate projections of future climate changes.

Application of the AI2 Climate Emulator to E3SMv2's Global Atmosphere Model, With a Focus on Precipitation Fidelity

AbstractCan the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.

Inferences of water–ice cloud physics determined from MAVEN/IUVS aerosol retrievals and Mars GCM comparisons

We performed retrievals of dust and water–ice optical depths at select times spanning Mars years (MYs) 33 through 36. We used data taken by the Imaging Ultraviolet Spectrograph (IUVS) instrument aboard the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft, whose precessing orbit allowed IUVS to image the diurnal evolution of water–ice clouds at local times throughout this MY range where they have not been studied. We created a radiative-transfer retrieval algorithm and used it to simultaneously fit the dust and water–ice optical depth of each usable spectrum in IUVS apoapse data for the majority of the MAVEN mission. We compared our results to other datasets to ensure our retrievals produced plausible values and discuss potential reasons for discrepancies. We obtained global climate model (GCM) simulations from two state-of-the-art models and compared our results to both sets of simulations. We show that there are significant differences between our water–ice retrievals and both sets of simulations, both in the spatial cloud patterns and column-integrated optical depths. We discuss the different approaches both models used for simulating these clouds and the implications for accurately modeling these clouds in future GCM work.

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

AbstractAlthough the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Carbon Cycle Instability for High-CO2 Exoplanets: Implications for Habitability

Implicit in the definition of the classical circumstellar habitable zone (HZ) is the hypothesis that the carbonate-silicate cycle can maintain clement climates on exoplanets with land and surface water across a range of instellations by adjusting atmospheric CO2 partial pressure (pCO2). This hypothesis is made by analogy to the Earth system, but it is an open question whether silicate weathering can stabilize climate on planets in the outer reaches of the HZ, where instellations are lower than those received by even the Archean Earth and CO2 is thought likely to dominate atmospheres. Since weathering products are carried from land to ocean by the action of water, silicate weathering is intimately coupled to the hydrologic cycle, which intensifies with hotter temperatures under Earth-like conditions. Here, we use global climate model simulations to demonstrate that the hydrologic cycle responds counterintuitively to changes in climate on planets with CO2-H2O atmospheres at low instellations and high pCO2, with global evaporation and precipitation decreasing as pCO2 and temperatures increase at a given instellation. Within the Maher & Chamberlain (or MAC) weathering formulation, weathering then decreases with increasing pCO2 for a range of instellations and pCO2 typical of the outer reaches of the HZ, resulting in an unstable carbon cycle that may lead to either runaway CO2 accumulation or depletion of CO2 to colder (possibly snowball) conditions. While the behavior of the system has not been completely mapped out, the results suggest that silicate weathering could fail to maintain habitable conditions in the outer reaches of the nominal HZ.

STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition

AbstractHigh‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally.

Multi‐Year Potential Predictability of the Wintertime Heavy Precipitation Potentials in East Asia

AbstractWe validate the multiyear potential predictability of wintertime heavy precipitation potential in East Asia by combining initialized decadal hindcasts of the global climate model and large ensemble simulations from a high‐resolution global atmospheric model. By analyzing a set of initialized hindcasts, the major predictive components of sea surface temperature (SST) variability beyond interannual timescales are identified as high‐latitudes multidecadal variability and the so‐called trans‐basin variability (TBV). A set of 100 ensemble simulations using a high‐resolution atmospheric model showed a significantly large signal‐to‐noise ratio for the wintertime heavy precipitation potential in East Asia, which is closely related to the TBV. When the SST around the maritime continent is higher, the anomalously low pressure in the northwestern Pacific enhances low‐level cold air transport due to the winter monsoon. Consequently, the resultant weaker baroclinicity in the lower atmosphere reduces storm activity and wintertime heavy precipitation potential in East Asia.

Sequential learning of climate change via a physical-parameter-based state-space model and Bayesian inference

Flexible decision-making strategies provide an alternative option for climate adaptation by considering future learning of climate change. A physical-parameter-based state-space model (SSM) with Bayesian inference is developed in this work to investigate reduction of uncertainty from more observations and facilitate flexible adaptation strategies. This SSM method integrates a two-layer, energy-balance model to describe global mean temperature response, models multiple sources of uncertainty such as climate sensitivity and aerosol forcing, and uses the informative priors from processing Global Climate Model simulations. Focusing on global mean temperature anomaly, which has important implications on policies and related impacts, the SSM is assessed by applying it to both historical and pseudo-observations (i.e., model simulations used as observations), assessing the posterior probabilities of physical parameters, and evaluating reduction of projection uncertainty. Some limitations of the method are observed, such as the sensitivity related to the adopted forcing time series. Comparing the end-of-the-century projections of global mean temperature sequentially made at year 2020, 2050, and 2080 using pseudo-observations, the reduction of uncertainty from the SSM is evident: the range of 95% prediction intervals on average decreases from 1.9°C in 2020 to 1.0°C in 2050, and to 0.6°C in 2080 under the Shared Socioeconomic Pathway (SSP) 2–4.5 (or from 2.7°C, to 1.2°C and to 0.7°C under SSP5-8.5). These results illustrate how the SSM framework provides probabilistic projections of climate change that can be sequentially updated with more observations, and this process can facilitate flexible adaptation strategies.

Air quality and public health effects of dairy digesters in California

The dairy industry in California emits large amounts of methane (CH4) that contributes significantly to the state's overall Greenhouse Gas (GHG) budget. Reducing CH4 emissions has become a key priority for dairy farms in support of California's GHG reduction goals. Anaerobic digesters designed to capture CH4 from animal manure present a practical option for reducing CH4 emissions, but a comprehensive evaluation of the local air quality impacts of this technology has not been previously undertaken. The simplest digester configurations decrease local emissions of volatile organic compounds (VOCs) but increase emissions of oxides of nitrogen (NOx), potentially changing local air quality. Here, we evaluate the air quality implications of widespread digester adoption in the year 2050 across the San Joaquin Valley (SJV) in central California, which is home to the highest concentration of dairy farms in the state. Changes to concentrations of air pollutants including ozone (O3), airborne particulate matter with diameter smaller than 2.5 μm (PM2.5), and various PM2.5 chemical components are predicted using the UCD/CIT chemical transport model at 4 km resolution. Dairy digester adoption is evaluated within two regional energy scenarios, including a business as usual (BAU) scenario and an 80% greenhouse gas reduction (GHGAi) scenario, to consider potential changes to the chemical regime that governs formation of secondary air pollution. Concentrations are evaluated across 32 randomly selected weeks over a 10-year period from the year 2046–2055 to establish a long-term average impact in the presence of El Niño Southern Oscillation (ENSO) variability. Regional weather patterns are downscaled from Global Climate Model simulations under the RCP8.5 global scenario. The results indicate that baseline dairy emissions make minor contributions to air pollutant concentrations in 2050. Under a worst-case scenario for digester adoption, PM2.5 concentrations would increase by 0.06 μg/m3 (current standard = 9 μg/m3), and maximum daily 8-h average (MDA8) O3 would change by −1.0 ppb to +0.2 ppb depending on the surrounding regional energy scenario (current standard = 70 ppb). A health impact analysis shows that the widespread use of dairy digesters would result in fewer than 0.1 additional deaths per 100,000 people due to changing air pollution. For comparison, this level of mortality change is more than 100 times smaller than the risk posed by seasonal flu. Further, Environmental Justice analysis indicates that the implementation of digesters will not influence the exposure disparities among different racial groups in either the SJV or the surrounding San Francisco Bay & Sacramento area. These findings suggest that dairy digesters can be widely adopted in central California to reduce GHG emissions with minimal effect on regional air quality and public health.

Powering aircraft with 100 % sustainable aviation fuel reduces ice crystals in contrails

Abstract. Powering aircraft by sustainable aviation fuels (SAFs) is a pathway to reduce the climate impact of aviation by lowering aviation lifecycle CO2 emissions and by reducing ice crystal numbers and radiative forcing from contrails. While the effect of SAF blends on contrails has been measured previously, here we present novel measurements on particle emission and contrails from 100 % SAF combustion. During the ECLIF3 (Emission and CLimate Impact of alternative Fuels) campaign, a collaboration between the Deutsches Zentrum für Luft- und Raumfahrt (DLR), Airbus, Rolls-Royce, and Neste, the DLR Falcon 20 research aircraft performed in situ measurements following an Airbus A350-941 source aircraft powered by Rolls-Royce Trent XWB-84 engines in 1 to 2 min old contrails at cruise altitudes. Apparent ice emission indices of 100 % HEFA-SPK (hydro-processed esters and fatty acids–synthetic paraffinic kerosene) were measured and compared to Jet A-1 fuel contrails at similar engine and ambient ice-supersaturated conditions within a single flight. A 56 % reduction in ice particle numbers per mass of burned fuel was measured for 100 % HEFA-SPK compared to Jet A-1 under engine cruise conditions. The measured 35 % reduction in soot particle numbers suggests reduced ice activation by the low-sulfur HEFA fuel. Contrail properties are consistently modeled with a contrail plume model. Global climate model simulations for the 2018 fleet conservatively estimate a 26 % decrease in contrail radiative forcing and stronger decreases for larger particle reductions. Our results indicate that higher hydrogen content fuels as well as clean engines with low particle emissions may lead to reduced climate forcing from contrails.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

The radiative and dynamical impact of clouds in the atmosphere of the hot Jupiter WASP-43 b

Context. Hot Jupiters are tidally locked gaseous exoplanets that exhibit large day-night temperature contrasts. Their cooler nightsides are thought to host clouds, as has been suggested by numerous observations. However, the exact nature of these clouds, their spatial distribution, and their impact on atmospheric dynamics, thermal structure, and spectra is still unclear. Aims. We investigate the atmosphere of WASP-43 b, a short period hot Jupiter recently observed with James Webb Space Telescope (JWST), to understand the radiative and dynamical impact of clouds on the atmospheric circulation and thermal structure. We aim to understand the impact of different kinds of condensates potentially forming in WASP-43 b, with various sizes and atmospheric metallicities. Methods. We used a 3D global climate model (GCM) with a new temperature-dependent cloud model that includes radiative feedbacks coupled with hydrodynamical integrations to study the atmospheric properties of WASP-43 b. We produced observables from our GCM simulations and compared them to spectral phase curves from various observations to derive constraints on the atmospheric properties. Results. We show that clouds have a net warming effect, meaning that the greenhouse effect caused by clouds is stronger than the albedo cooling effect. We show that the radiative effect of clouds has various impacts on the dynamical and thermal structure of WASP-43 b. Depending on the type of condensates and their sizes, the radiative-dynamical feedback will modify the horizontal and vertical temperature gradient and reduce the wind speed. For super-solar metallicity atmospheres, fewer clouds form in the atmosphere, leading to a weaker feedback. Comparisons with spectral phase curves observed with HST, Spitzer, and JWST indicate that WASP-43 b's nightside is cloudy and rule out sub-micron Mg2SiO4 cloud particles as the main opacity source. Distinguishing between cloudy solarand cloudy super-solar-metallicity atmospheres is not straightforward, and further observations of both reflected light and thermal emission are needed.

Contribution of Anthropogenic Activities to the Intensification of Heat Index‐Based Spatiotemporally Contiguous Heatwave Events in China

AbstractIn this study, we identified heat index‐based spatiotemporally contiguous heatwaves (HI‐STHWs) in China based on meteorological observations and Coupled Model Intercomparison Project Phase 6 global climate model simulations. We analyzed the spatiotemporal patterns of changes in HI‐STHWs in the past and future and quantitatively attributed these changes to anthropogenic activities. The results show that the duration, severity, average, maximum, and total impacted area of the annual strongest HI‐STHWs during the present period of 1991–2014 are 1.77, 2.0, 1.05, 1.14, and 1.89 times the historical period of 1961–1990, respectively. In the fingerprint results, the anthropogenic greenhouse gases (GHG) signal is significantly detected, while the aerosol (AER) and natural (NAT) signals are not. GHG is the primary factor driving the intensification of HI‐STHWs, which alone explains about 130%, 122%, 112%, 111%, and 114% of the above changes. The reason for GHG contribution exceeding 100% is that AER might have a negative contribution although nonsignificant. In the future warming climate, anthropogenic activities are projected to lead to more unprecedented HI‐STHWs. Under the high emissions scenario of SSP585, by 2100, the annual strongest HI‐STHW in China is projected to last almost the whole year and influence 96% regions of China in the most serious day. Meanwhile, its duration and total impacted area are 24.5 [17.2, 31.6] (90% confidence interval) and 107.2 [70, 129.9] times the preindustrial period. However, if the warming level could be limited to 2/1.5°C, those values would be 3.4/5.4 and 8.2/16.2 times smaller than that under the SSP585 scenario by 2100.

Resilient Antarctic monsoonal climate prevented ice growth during the Eocene

Abstract. Understanding the extreme greenhouse of the Eocene (56–34 Ma) is key to anticipating potential future conditions. While providing an end member towards a distant high-emission scenario, the Eocene climate also challenges the different tools at hand to reconstruct such conditions. Besides remaining uncertainty regarding the conditions under which the large-scale glaciation of Antarctica took place, there is poor understanding of how most of the continent remained ice free throughout the Eocene across a wide range of global temperatures. Seemingly contradictory indications of ice and thriving vegetation complicate efforts to explain the Antarctic Eocene climate. We use global climate model simulations to show that extreme seasonality mostly limited ice growth, mainly through high summer temperatures. Without ice sheets, much of the Antarctic continent had monsoonal conditions. Perennially mild and wet conditions along Antarctic coastlines are consistent with vegetation reconstructions, while extreme seasonality over the continental interior promoted intense weathering shown in proxy records. The results can thus explain the coexistence of warm and wet conditions in some regions, with small ice caps forming near the coast. The resilience of the climate regimes seen in these simulations agrees with the longevity of warm Antarctic conditions during the Eocene but also challenges our view of glacial inception.

Precipitation Scaling in Extreme Rainfall Events and the Implications for Future Indian Monsoon: Analysis of High‐Resolution Global Climate Model Simulations

AbstractThe increase in water holding capacity of the atmosphere with temperature, given by the Clausius‐Clapeyron (CC) relationship, describes the changes in extreme rainfall intensities at warmer atmospheric states. We study the characteristics of extreme rainfall events (EREs) during the Indian summer monsoon season with respect to thermodynamic changes and precipitation‐scaling over the Indian subcontinent and its homogeneous rainfall zones. We utilize outputs from a present‐day climate simulation and a time‐slice future climate change projection experiments of a high‐resolution global climate model. Large changes are seen for very EREs (vEREs) which suggests their sensitivity to warmer temperatures. In future, the altered radiative forcing will heat up the upper atmosphere, stabilize it and offset the effect of increasing humidity on precipitation intensity. Our analysis also suggests that more convective clouds and the interplay of increased moisture content and circulation will result in future changes in EREs.

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

The study examines the characteristics and variability of monsoon rainfall in Myanmar, focusing on the relationship between heavy rainfall, floods, and earthquakes, which impact agriculture, hydrology, and the environment. Generally, heavy rainfall can cause flooding, economic losses, and water table changes. Northern Myanmar floods occur mainly during the monsoon season from June to October and can be classified into widespread floods along major rivers like Ayeyarwady, Thanlwin, Chindwin, and Sittoung and flash floods in small streams and rivers. Climate change is expected to increase the frequency and intensity of extreme weather events, including heavy rainfall, which can trigger floods or landslides, which also can in turn cause earthquakes. Heavy rainfall over northern Myanmar and the Sagaing faults, which are the main triggers of earthquakes, has been the subject of several studies. The study uses the Copernicus 5 database of global climate model (GCM) simulations with two scenario analyses on climate change detection and indices (ETCCDI) to study changes in climatic extremes. Results show high intensity in the northern region and monsoon core regions, while the central region shows less intensity. The study also uses intensity-duration-frequency (IDF) curves to analyze the relationship between rainfall duration, intensity, and return time in major risk zones. The study finds that as short duration lengthens, rainfall intensity increases for future rainfall patterns. This information is expected to be convenient for local authorities and flood protection projects in rural and urban basins.