Global Climate Model Simulations Research Articles

Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

Diagnosing Mechanisms of Hydrologic Change under Global Warming in the CESM1 Large Ensemble

Abstract Global warming is expected to cause significant changes in the pattern of precipitation minus evaporation (P − E), which represents the net flux of water from the atmosphere to the surface or, equivalently, the convergence of moisture transport within the atmosphere. In most global climate model simulations, the pattern of P − E change resembles an amplification of the historical pattern—a tendency known as “wet gets wetter, dry gets drier.” However, models also predict significant departures from this approximation that are not well understood. Here, we introduce a new method of decomposing the pattern of P − E change into contributions from various dynamic and thermodynamic mechanisms and use it to investigate the response of P − E to global warming within the CESM1 Large Ensemble. In contrast to previous decompositions of P − E change, ours incorporates changes not only in the monthly means of atmospheric winds and moisture, but also in their temporal variability, allowing us to isolate the hydrologic impacts of changes in the mean circulation, transient eddies, relative humidity, and the spatial and temporal distributions of temperature. In general, we find that changes in the mean circulation primarily control the P − E response in the tropics, while temperature changes dominate at higher latitudes. Although the relative importance of specific mechanisms varies by region, at the global scale departures from the wet-gets-wetter approximation over land are primarily due to changes in the temperature lapse rate, while changes in the mean circulation, relative humidity, and horizontal temperature gradients play a secondary role.

Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions

Weighting climate models has recently become a more accepted approach. However, it remains a topic of ongoing discussion, especially for analyses needed at regional scales, such as hydrological assessments. Various studies have evaluated the weighting approaches for climate simulations. Yet, few case studies have assessed the impacts of weighting climate models on streamflow projections. Additionally, the methodological and location limitations of previous studies make it difficult to extrapolate their conclusions over regions with contrasting hydroclimatic regimes, highlighting the need for further studies. Thus, this study evaluates the effects of different climate model’s weighting approaches on hydrological projections over hydrologically diverse basins. An ensemble of 24 global climate model (GCM) simulations coupled with a lumped hydrological model is used over 20 North American basins to generate 24 GCM-driven streamflow projections. Six unequal-weighting approaches, comprising temperature-, precipitation-, and streamflow-based criteria, were evaluated using an out-of-sample approach during the 1976–2005 reference period. Moreover, the unequal-weighting approaches were compared against the equal-weighting approach over the 1976–2005, 2041–2070, and 2070–2099 periods. The out-of-sample assessment showed that unequally weighted ensembles can improve the mean hydrograph representation under historical conditions compared to the common equal-weighting approach. In addition, results revealed that unequally weighting climate models not only impacted the magnitude and climate change signal, but also reduced the model response uncertainty spread of hydrological projections, particularly over rain-dominated basins. These results underline the need to further evaluate the adequacy of equally weighting climate models, especially for variables with generally larger uncertainty at regional scale.

Incorporating Weather Attribution to Future Water Budget Projections

Weather attribution is a scientific study that estimates the relative likelihood of an observed weather event occurring under different climate regimes. Water budget models are widely used tools that can estimate future water resource management and conservation conditions using daily weather forcing. A stochastic weather generator (WG) is a statistical model of daily weather sequences designed to simulate or represent a climate description. A WG provides a means to generate stochastic, future weather forcing to drive a water budget model to produce future water resource projections. Observed drought magnitude and human-induced climate change likelihood from a weather attribution study provide targets for WG calibration. The attribution-constrained WG approximately reproduces the five-fold increase in probability attributed to observed drought magnitude under climate change. A future (2031–2060) climate description produced by the calibrated WG is significantly hotter, with lower expected soil moisture than the future description obtained from global climate model (GCM) simulation results. The attribution-constrained WG describes future conditions where historical extreme and severe droughts are significantly more likely to occur.

Can Sub‐Daily Multivariate Bias Correction of Regional Climate Model Boundary Conditions Improve Simulation of the Diurnal Precipitation Cycle?

AbstractThe diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias‐corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias‐corrected lateral and lower boundary conditions. Results show that the RCMs with bias‐corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub‐daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short‐term intense precipitation occurs.

Application of deep learning algorithms to correct bias in CMIP6 simulations of surface air temperature over the Indian monsoon core region

AbstractIndian subcontinent witnessed a rise in surface air temperature (SAT) in recent decades, during the summer months of March, April and May. The monsoon core region (MCR) of India experiences a hot and humid climate, with temperatures typically highest in May and June before the onset of the monsoon. Global climate model (GCM) simulations of SAT are very much essential to understand the future climate of Indian MCR. Biases in GCMs simulations are due to insufficient knowledge of parameterizations and various assumptions that are made to simulate the complex interactions between land, ocean and atmosphere. The objective of this study is to correct the bias in the Coupled Model Intercomparison Project Phase 6 (CMIP6)–GCM simulations of SAT during March, April and May months over MCR for the historical period 1985–2014 and shared socio‐economic pathways (SSPs) SSP2‐4.5 and SSP5‐8.5 for the period 2015–2100. SAT dataset of fifth‐generation reanalysis (ERA5) of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used as reference dataset to perform bias correction for the historical period. Preliminary investigation of both SAT datasets has shown that there exists considerable warm bias (1.47°C) over the MCR. Bias correction is performed using a one‐dimensional convolutional neural network (CNN‐1D) and a convolutional long short‐term memory network (CNN‐LSTM) deep learning algorithm. The performance of these algorithms is evaluated with the statistical metrics such as root‐mean‐square error (RMSE), normalized root‐mean‐square error, Nash–Sutcliffe efficiency, mean absolute error, percent bias, correlation coefficient and dynamic time warping. RMSE and percent bias were decreased to 0.35°C and 0.8% with CNN‐LSTM algorithm. The CNN‐LSTM algorithm also preserves the year‐to‐year variability of SAT. Hence, CNN‐LSTM algorithm is found to be suitable for the bias correction of GCM simulations of SAT with encouraging results.

Emerging extreme heat conditions as part of the new climate normal

As a result of global warming, extreme heat conditions have become more frequent and severe. This will likely continue or accelerate in the future, particularly under high radiative forcing scenarios. In the present study, based on an ensemble of global climate model simulations, we identify the absolute historical extremes expressed by several temperature indices. Considering projections under two future pathways (SSP1-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}2.6, SSP5-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}8.5), we investigate to what extent extreme heat conditions will become predominant during the rest of the century. The timing of a transition to prevailing hot weather extremes is critical for the development of mitigation and adaptation strategies; therefore, we also identify the projected first year of such a transition, as well as the persistence in subsequent decades. Different aspects of heat extremes are investigated, including both maximum and minimum temperature. For some climate zones, our results highlight that regardless of mitigation efforts, hot weather conditions will be at least similar but likely more harsh compared to historical extremes within the following decades. By the end of the century, under a business-as-usual pathway, successive years will be much more extreme than the most severe conditions in the recent past virtually everywhere.

Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

Abstract Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

AbstractRadiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases.

MJO in Different Orbital Regimes: Role of the Mean State in the MJO’s Amplitude during Boreal Winter

Abstract The Madden–Julian oscillation (MJO) exhibits pronounced seasonality, with one of the key unanswered questions being the following: what controls the maximum in MJO precipitation variance in the Southern Hemisphere during boreal winter? In this study, we examine a set of global climate model simulations in which the eccentricity and precession of Earth’s orbit are altered to change the boreal winter mean state in an attempt to reveal the processes that are responsible for the MJO’s amplitude in the boreal winter. In response to the forced insolation changes, the north–south asymmetry in sea surface temperature is amplified in boreal fall, which intensifies the Hadley circulation in boreal winter. The stronger Hadley circulation yields higher mean precipitation and stronger mean lower-tropospheric westerlies in the southern part of the Indo-Pacific warm pool. The MJO precipitation variability increases significantly where the mean precipitation and lower-tropospheric westerlies strengthen. In the column-integrated moisture budget of the simulated MJO, only surface latent heat flux feedback shows a trend that is consistent with the MJO’s amplitude, suggesting an important role for the surface latent heat flux feedback in the MJO’s amplitude during the boreal winter. An analysis of the moisture–precipitation relationship in the simulations shows that the increase in the mean precipitation lowers the convective moisture adjustment time scale, leading to the increase in precipitation variance. Our results suggest that the mean-state precipitation plays a critical role in the maintenance mechanism of the MJO.

More Than Half of Emitted Black Carbon Is Missing in Marine Sediments

Marine sediments are the ultimate reservoir for black carbon (BC) preservation, and BC burial in sediment/soils is an efficient method for carbon sequestration to mitigate CO2 emissions. A portion of soil charcoal and atmospheric BC is dissolved in inland and oceanic water, but the amount of BC in the ocean remains unclear. We analyzed multi-sediment cores from the northwestern Pacific Ocean and lakes in China and reconstructed the timeline of BC deposition from 1860 to ~2012. The lacustrine sediment cores showed an increase in BC deposition by a factor of 4–7 during the industrialization period in China compared to the years 1860–1950 (reference level). Such increasing trends in BC have also been reproduced by ten global climate model simulations. However, the marine sediment cores did not retain these significant increases in BC deposition. Meanwhile, the model simulations predicted increased trends compared to the observed flat trends of BC deposition in marine sediments. The discrepancy suggests a large amount of BC, i.e., 65 (±11)%, is missing in marine sediment sinks. Thus, since more than half of emitted BC has dissolved into oceanic water, the dissolved BC and carbon cycle should be reconsidered in the global carbon budget.

Evaluation of CMIP6 HighResMIP Models and ERA5 Reanalysis in Simulating Summer Precipitation over the Tibetan Plateau

The High Resolution Model Intercomparison Project (HighResMIP) experiment within the Coupled Model Intercomparison Project Phase 6 (CMIP6) has enabled the evaluation of the performance of climate models over complex terrain for the first time. The study aims to evaluate summer (June to August) precipitation characteristics over the Tibetan Plateau (TP). Precipitation derived from HighResMIP models and ERA5 are compared against the China Merged Precipitation Analysis (CMPA). The nineteen models that participated in HighResMIP are classified into three categories based on their horizontal resolution: high resolution (HR), middle resolution (MR), and low resolution (LR). The multimodel ensemble means (MMEs) of the three categories of models are evaluated. The spatial distribution and elevation dependency of the hourly precipitation characteristics, which include the diurnal peak hour, diurnal variation amplitude, and frequency–intensity structure, are our main focus. The MME-HR and ERA5 both show comparable ability in simulating precipitation in the TP. The MME-HR has a smaller deviation in the precipitation amount and diurnal variation at various altitudes. The ERA5 can better simulate the elevation dependence of the frequency–intensity structure, but its elevation dependence of diurnal variation shows a trend opposite to the observations. Although the MME-HR produces the best simulation results among the three MMEs, the simulation effects of HighResMIP’s precipitation in the TP do not necessarily improve with increasing the horizontal resolution from LR to MR. The finer model resolution has a small impact on the simulation effect of precipitation intensity, but the coarser model resolution will limit the generation of heavy precipitation. These findings give intensive measures for evaluating precipitation in complex terrain and can help us in comprehending rainfall biases in global climate model simulation.

Spin evolution of Venus-like planets subjected to gravitational and thermal tides

Context. The arrival of powerful instruments will provide valuable data for the characterization of rocky exoplanets. Rocky planets are mostly found in close-in orbits. They are therefore usually close to the circular-coplanar orbital state and are thus considered to be in a tidally locked synchronous spin state. For planets with larger orbits, however, exoplanets should still have nonzero eccentricities and/or obliquities, and realistic models of tides for rocky planets can allow for higher spin states than the synchronization state in the presence of eccentricities or obliquities. Aims. This work explores the secular evolution of a star–planet system under tidal interactions, both gravitational and thermal, induced by the quadrupolar component of the gravitational potential and the irradiation of the planetary surface, respectively. We show the possible spin–orbit evolution and resonances for eccentric orbits and explore the possibility of spin-orbit resonances raised by the obliquity of the planet. Then, we focus on the additional effect of a thick atmosphere on the possible resulting spin equilibrium states and explore the effect of the evolution of the stellar luminosity. Methods. We implemented the general secular evolution equations of tidal interactions in the secular code called ESPEM. In particular, we focus here on the tides raised by a star on a rocky planet and consider the effect of the presence of an atmosphere, neglecting the contribution of the stellar tide. The solid part of the tides was modeled with an anelastic rheology (Andrade model), while the atmospheric tides were modeled with an analytical formulation that was fit using a global climate model simulation. We focused on a Sun-Venus-like system in terms of stellar parameters, orbital configuration and planet size and mass. The Sun-Venus system is a good laboratory for studying and comparing the possible effect of atmospheric tides, and thus to explore the possible spin state of potential Venus-like exoplanets. Results. The formalism of Kaula associated with an Andrade rheology allows spin orbit resonances on pure rocky worlds. Similarly to the high-order spin–orbit resonances induced by eccentricity, the spin obliquity allows the excitation of high-frequency Fourier modes that allow some spin-orbit resonances to be stable. If the planet has a dense atmosphere, like that of Venus, another mechanism, the thermal tides, can counterbalance the effect of the gravitational tides. We found that thermal tides change the evolution of the spin of the planet, including the capture in spin–orbit resonances. If the spin inclination is high enough, thermal tides can drive the spin toward an anti-synchronization state, that is, a the 1:1 spin–orbit resonance with an obliquity of 180 degrees. Conclusions. Through our improvement of the gravitational and thermal tidal models, we can determine the dynamical state of exo-planets better, especially if they hold a thick atmosphere. In particular, the contribution of the atmospheric tides allows us to reproduce the spin state of Venus at a constant stellar luminosity. Our simulations have shown that the secular evolution of the spin and obliquity can lead to a retrograde spin of the Venus-like planet if the system starts from a high spin obliquity, in agreement with previous studies. The perturbing effect of a third body is still needed to determine the current state of Venus starting from a low initial obliquity. When the luminosity evolution of the Sun is taken into account, the picture changes. We find that the planet never reaches equilibrium: the timescale of the rotation evolution is longer than the luminosity variation timescale, which suggests that Venus may never reach a spin equilibrium state, but may still evolve.

The shift of decadal trend in Middle East dust activities attributed to North Tropical Atlantic variability

The Middle East, as the world’s second-largest dust source region, has dust emissions that significantly impact numerous populated areas, extending from North America to South Asia. Over the past two decades, dust activity in the Middle East has exhibited pronounced variability, with a notable trend shift from positive to negative around 2010. The underlying cause of this trend shift remains elusive. In this study, we employ multi-source datasets and global climate model simulations to demonstrate that the variability of Middle East dust activities is closely tied to changes in North Tropical Atlantic (NTA) sea surface temperature (SST). Specifically, a warm NTA SST anomaly generates an anomalous regional zonal cell characterized by ascending air motion above the NTA and descending air surrounding the Middle East. The associated surface high pressures around the Middle East subsequently induce hot and dry conditions accompanied by intensified Shamal winds in the north, which are favorable for dust emission and transport. The shift in SST trends from positive to negative in the NTA around 2010 is therefore responsible for the observed dust trend shift in the Middle East. This mechanism holds vital implications for predicting decadal dust variability over the Middle East region and further the project of global environment.

Global projections of flash drought show increased risk in a warming climate

Flash drought, characterized by unusually rapid drying, can have substantial impact on many socioeconomic sectors, particularly agriculture. However, potential changes to flash drought risk in a warming climate remain unknown. In this study, projected changes in flash drought frequency and cropland risk from flash drought are quantified using global climate model simulations. We find that flash drought occurrence is expected to increase globally among all scenarios, with the sharpest increases seen in scenarios with higher radiative forcing and greater fossil fuel usage. Flash drought risk over cropland is expected to increase globally, with the largest increases projected across North America (change in annual risk from 32% in 2015 to 49% in 2100) and Europe (32% to 53%) in the most extreme emissions scenario. Following low-end and medium scenarios compared to high-end scenarios indicates a notable reduction in annual flash drought risk over cropland.

Contrasting Responses of Atlantic and Pacific Tropical Cyclone Activity to Atlantic Multidecadal Variability

AbstractThis research assesses the influences of Atlantic Multidecadal Variability (AMV) on global tropical cyclones (TCs) using two large ensembles of idealized global climate model simulations with opposite signs of AMV forcings superimposed (i.e., AMV+ and AMV–). We first detect TCs and then compare TC activity by basin in the two AMV experiments. We find contrasting responses of Atlantic and Pacific TC frequency to the AMV anomalies. Compared to AMV–, AMV+ significantly increases TC frequency in the North Atlantic, including those making landfalls. The increase is explained by warmer sea surface temperature, higher relative humidity, increased relative vorticity, and weaker vertical wind shear under AMV+. By contrast, AMV+ decreases TC occurrence over the western North Pacific and South Pacific, which is tied to stronger vertical wind shear and lower relative humidity. The opposite responses of TC activity to AMV+ are attributed to strengthened Walker Circulation between the Atlantic and Pacific.

Northward Shifts of the Sahara Desert in Response to Twenty-First-Century Climate Change

Abstract The spatial extent of the Sahara (the largest nonpolar desert) has significant impacts on the livelihood of people residing in its surrounding areas. Despite the fact that climate change would foreseeably impact the location and size of the desert, its future responses (i.e., advance or retreat) are rarely explored in previous studies. Here, through the development of an ensemble Bayesian discriminant analysis approach, we use 10 of the latest high-resolution GCM (global climate model) simulations to document robust annual and seasonal responses of the Sahara Desert to twenty-first-century climate change, with the consideration of modeling uncertainties. We find northward shifts of the Sahara/Sahel and eastern expansion of the nondesert zone under both SSP2–4.5 and SSP5–8.5 scenarios, the former more pronounced in the wet season and the latter in the dry season. Countries located near the Mediterranean may thus experience higher risks of drought, while the projected retreat of the Saharan southern boundary will be beneficial to the local water availability of proximal countries. Significance Statement Given that sub-Saharan Africa is one of the most vulnerable regions to climate change, the Sahara’s expansion would bring unexpected health risks to billions of people. It is thus vital to understand its robust response to global warming. However, previous studies are merely focused on using a simple precipitation threshold as the definition criterion to estimate the varying size of the Sahara Desert. In addition, significant uncertainty in precipitation projections also limits relevant investigations of the Sahara’s future responses. Here, by developing an ensemble Bayesian discriminant analysis approach, we could provide an objective basis for desert identification under large intermodel uncertainty. Further, we find significant northward shifts of both the Sahara and the Sahel, which may induce higher risks of drought over the northwest of North Africa.

Assessing Three Perfect Prognosis Methods for Statistical Downscaling of Climate Change Precipitation Scenarios

AbstractUnder the perfect prognosis approach, statistical downscaling methods learn the relationships between large‐scale variables from reanalysis and local observational records. These relationships are subsequently applied to downscale future global climate model (GCM) simulations in order to obtain projections for the local region and variables of interest. However, the capability of such methods to produce future climate change signals consistent with those from the GCM, often referred to as transferability, is an important issue that remains to be carefully analyzed. Using the EC‐Earth GCM and focusing on precipitation, we assess the transferability of generalized linear models, convolutional neural networks and a posteriori random forests (APRFs). We conclude that APRFs present the best overall performance for the historical period, and future local climate change signals consistent with those projected by EC‐Earth. Moreover, we show how a slight modification of APRFs can greatly improve the temporal consistency of the downscaled series.

A 10-km CMIP6 downscaled dataset of temperature and precipitation for historical and future Vietnam climate

High-resolution climate projections are mandatory for many applications and impact assessments in environmental and management studies. In response to the needs in Vietnam, this study constructs a new precipitation and temperature daily dataset for Vietnam, at a high spatial resolution of 0.1° × 0.1°, based on the outputs of 35 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Bias Correction and Spatial Disaggregation (BCSD) method is adopted to bias-correct monthly GCM simulations using observation data, then subsequently temporally disaggregate them into daily data. The new dataset is called CMIP6-VN, covering the present-time period 1980–2014 and future projections for 2015–2099 from both CMIP6 tier-1 (Shared Socioeconomic Pathways (SSPs) 1–1.26, 2–4.5, 3–7.0, and 5–8.5) and tier-2 (SSPs 1–1.9, 4–3.4, 4–6.0) experiments. Results indicated the good performance of CMIP6-VN for the historical period, suggesting that the dataset could be used for studies on climate change assessment and impacts in Vietnam.

Clouds Increasingly Influence Arctic Sea Surface Temperatures as CO2 Rises

AbstractAs Arctic sea ice retreats during the melt season, the upper ocean warms in response to atmospheric heat fluxes. Overall, clouds reduce these fluxes in summer, but how the radiative impacts of clouds on ocean warming could change as sea ice declines has not been documented. In global climate model simulations with variable CO2, the timing of sea ice retreat strongly influences the amplitude of cloud‐induced summer cooling at the ocean surface. Under pre‐industrial CO2 concentrations, summer clouds have little direct effect on maximum annual sea surface temperatures (SST). When CO2 concentrations increase, sea ice retreats earlier, allowing more solar radiation to warm the ocean. Clouds can counteract this summer warming by reflecting solar radiation back to space. Consequently, clouds explain up to 13% more variability in maximum annual SST under modern‐day CO2 concentrations. Maximum annual SST are three times more sensitive to summer clouds when CO2 concentrations are four times pre‐industrial levels.