CMIP5 Climate Models Research Articles

КЛИМАТИЧЕСКИЕ ИЗМЕНЕНИЯ НА ТЕРРИТОРИИ ПРИВОЛЖСКОГО ФЕДЕРАЛЬНОГО ОКРУГА В XX-XXI ВЕКАХ И ИХ ПОСЛЕДСТВИЯ ДЛЯ АГРОСФЕРЫ

The spatial and temporal variability of the main climatic indicators on the territory of the Volga Federal District in the 20th-21st centuries is considered. A general increasing trend in air temperature is revealed, and a heterogeneous pattern of precipitation changes is shown. The temperature variations until the end of the 21st century are analyzed on the example of Kazan using 40 CMIP6 climate models for four anthropogenic scenarios. In the paper, there is an assessment of the dynamics of agroclimatic resources on the territory of the Volga Federal District: the duration of the growing season, the sum of positive temperatures, total precipitation, and photosynthetic radiation. A correlation has been found between the temperature fluctuations in the region and the atmospheric circulation indices (NAO, AO, SCAND, and EAWR). For the territory of Tatarstan, a degree of aridity and waterlogging is evaluated using the agroclimatic indices: the Budyko’s dryness index, the Selyaninov’s hydrothermal coefficient, the Sapozhnikova’s moisture index. It is shown that there is a tendency toward an increase in the duration of the growing season, its heat supply and aridity in the region in summer. A statistical estimation of the dependence of spring wheat yield on the agroclimatic indices on the territory of the Republic of Tatarstan is given.

Simulations and Projections of Winter Sea Ice in the Barents Sea by CMIP6 Climate Models

Simulations and Projections of Winter Sea Ice in the Barents Sea by CMIP6 Climate Models

Spatio-Temporal Variation of Critical Relative Humidity Based on Multiple Datasets

Clouds remain an important source of uncertainty in climate simulations, in large part because subgrid processes are not well represented. Critical relative humidity (RHc) is an important metric for subgrid-scale variability in humidity in cloud parameterization. Based on CloudSat and CALIPSO satellite data, we explored the spatial and temporal distribution characteristics of RHc, assessed the ability of ERA-5 and MERRA-2 reanalysis and CMIP-6 climate models to characterise humidity subgrid variability and further explored the influence of meteorological factors and aerosols. The statistical results showed that there was significant variation in the spatial distribution of RHc, with large variations in both latitude and altitude, as well as more pronounced monthly variations, and that there were differences in monthly variations between regions. Both the reanalysis data and the climate models were able to reproduce similar spatial and temporal distribution patterns but differed significantly in their specific values. The temporal correlations with satellite observations were also relatively poor. In addition, aerosols and meteorological conditions affected the distribution of RHc by influencing the cloud fraction at a certain relative humidity level, indicating that their influence needs to be considered in future parameterization schemes.

Storylines of Sahel Precipitation Change: Roles of the North Atlantic and Euro‐Mediterranean Temperature

AbstractFuture changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained.

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

AbstractExtratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

Overturning Pathways Control AMOC Weakening in CMIP6 Models

AbstractFuture projections indicate the Atlantic Meridional Overturning Circulation (AMOC) will weaken and shoal in response to global warming, but models disagree widely over the amount of weakening. We analyze projected AMOC weakening in 27 CMIP6 climate models, in terms of changes in three return pathways of the AMOC. The branch of the AMOC that returns through diffusive upwelling in the Indo‐Pacific, but does not later upwell in the Southern Ocean (SO), is particularly sensitive to warming, in part, because shallowing of the deep flow prevents it from entering the Indo‐Pacific via the SO. The present‐day strength of this Indo‐Pacific pathway provides a strong constraint on the projected AMOC weakening. However, estimates of this pathway using four observationally based methods imply a wide range of AMOC weakening under the SSP5‐8.5 scenario of 29%–61% by 2100. Our results suggest that improved observational constraints on this pathway would substantially reduce uncertainty in 21st century AMOC decline.

Performance evaluation of CMIP6 climate models for selecting a suitable GCM for future precipitation at different places of Tamil Nadu.

Climate change refers to long-term variations in climate parameters. Future climate information can be projected using a GCM (General Circulation Model). Identifying a particular GCM is crucial for climate impact studies. Researchers are perplexed about selecting a suitable GCM for downscaling to predict future climate parameters. Recent updates to CMIP6 global climate models have included shared socioeconomic pathways based on the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment Report (AR6). The performance of 24 CMIP6 GCMs in precipitation with a multi-model ensemble filter was compared to IMD (India Meteorological Department)0.25 × 0.25 degreesrainfall data in Tamil Nadu. The performance was evaluated with the help of Compromise Programming (CP), which involves metrics such as R2 (Pearson correlation co-efficient), PBIAS (Percentage Bias), NRMSE (Normalized Root Mean Square Error), and NSE (Nash-Sutcliffe Efficiency). The GCM ranking was performed through Compromise programming by comparing the IMD data and GCM data. The results of the CP analyses of the statistical metrics suggest thatthe suitable GCMs for the North-East monsoon are CESM2 for Chennai, CAN-ESM5 for Vellore, MIROC6 for Salem, BCC-CSM2-MR for Thiruvannamalai, MPI-ESM-1-2-HAM for Erode, MPI-ESM1-2-LR for Tiruppur, MPI-ESM1-2-LR for Trichy, MPI-ESM1-2-LR for Pondicherry, MPI-ESM1-2-LR for Dindigul, CNRM-CM6-HR for Thanjavur, MPI-ESM1-2-LR for Thirunelveli and UKESM1-0-LL for Thoothukudi. The appropriate suitable GCMs for South-West monsoon as CESM2 is appropriate for Chennai, IPSL-CM6A-LR for Vellore, CESM2-WACCM-FV2 for Salem, CAMS-CSM1-0 for Thiruvannamalai, MPI-ESM-1-2-HR for Erode, MPI-ESM-1-2-HR for Tiruppur, EC- EARTH3 for Trichy, EC- EARTH3 for Pondicherry, MPI-ESM-1-2-HR for Dindigul, CESM2-FV2 for Thanjavur, ACCESS-CM2 for Thirunelveli and ACCESS-CM2 for Thoothukudi respectively. This study emphasizes the importance of selecting an appropriate GCM. Selecting a suitable GCM will be useful in climate change impact studies and there by suggesting necessary adaptation and mitigation strategies.

MODERN AND PROJECTED CHARACTERISTICS OF SEASONAL CYCLE OF ICE COVER IN THE RUSSIAN ARCTIC SEAS

The characteristics of the sea ice conditions in the Seas of the Russian Arctic during the period of satellite measurements were studied and quantitative estimates of their changes in the second half of the 21st century were made based on the results of the CMIP6 climate models’ ensemble using aggressive and moderate scenarios of anthropogenic forcing, SSP585 and SSP245 respectively. It has been revealed that the dates of closing (DOC) of the sea ice cover and the length of the open water season (LOWS) are in general successfully reproduced by the ensemble of models. It is shown that DOC on average over the studied Seas will be observed about forty days later under SSP245 scenario and two months later under SSP585 scenario in 2061–2080 compared to 1981–2020 period. At the same time, the increase of LOWS is more than two and a half months under SSP245 scenario and almost four months under SSP585 scenario. The most significant changes in DOC are expected in the north of the Barents and Kara Seas, as well as in the northwest of the Laptev Sea: 2–2.5 months and 2.5–3 months later for the SSP245 and SSP585 scenarios respectively.

Projected impacts of climate change on viticulture over French wine regions using downscaled CMIP6 multi-model data

Climate change is a major challenge for the French wine industry. Climatic conditions in French vineyards have already changed and will continue to evolve impacting viticulture.This study aims to analyse the evolution of agro- and eco-climatic indices based on phenology simulation of French wine-growing regions. This evolution was analysed on a recent-past period (1962–1991 to 1992–2021) using SAFRAN climate data and on a future projected period (1985–2014 to 2041–2070) with two SSP trajectories (SSP2-4.5 and SSP5-8.5). A set of 19 CMIP6 climate models downscaled at 8 km grid resolution over France coupled with three phenological and a water balance model were used. Phenological model parameters and training system characteristics were adapted to each region to match as much as possible current practices.Temperatures during the growing season have increased by +1 °C to +2.1 °C since the second half of the 20th century and could rise to +3.7 °C in regions around the Mediterranean by 2070. The inter-model variance concerning the precipitation is high, a significant change (decrease) in precipitation during the grapevine growing season is observed only for the regions of western France (Oceanic climate) over the period 2040–2071 with the SSP5 trajectory. All simulated phenological stages have shifted toward earlier dates. Their occurrence should be even earlier by 2070 with an average advance of up to 22 days for the mid-veraison of Pinot noir in eastern France. The theoretical maturity date (sugar content) should also be advanced from 19 to 30 days depending on the considered region and SSP. Thermal conditions closer to the photosynthetic optimum should promote onset by the early second half of the 21st century. The increase in both the number of hot days and grapevine water deficit during the period of fruit development should impact grape production in quality and quantity in all wine-growing regions. Spring frost projections show no significant change in risk for the second half of the 21st century, compared to current conditions.

Selection and downscaling of CMIP6 climate models in Northern Nigeria

Selection and downscaling of CMIP6 climate models in Northern Nigeria

Projections of Global Drought and Their Climate Drivers Using CMIP6 Global Climate Models

Due to the complex coupling between drought and climatic factors, the future drought conditions that might occur under climate change is still unclear. In this research, we used the daily SPEI algorithm to project global drought conditions during 2016–2100 based on the data from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We also employed partial correlation analysis to explore the influence of climate factors on drought. Our analyses show the following: (1) Drought conditions projected by CMIP6 under different models are similar; however, they can vary widely across regions. (2) According to the MK trend test, drought conditions in most regions around the world are expected to become increasingly severe in the future, and this trend is significant. (3) Based on the results of the partial correlation analysis results, it is understood that drought events in most regions worldwide are primarily driven by precipitation. This study contributes to the discussion of projecting future drought conditions and expands the application by utilizing the state−of−the−art CMIP6 climate models and scenarios.Highlight

Impacts of climate change on dragonflies and damselflies in West and Central Asia

AbstractAimTo project the impact of climate change on dragonfly and damselfly diversity in West and Central Asia.LocationWest and Central Asia.Time period1900–2020 data used to predict distributions in 2070 and 2100.Taxon studiedOdonata.MethodsBased on 149,001 records, distribution models were created for 159 species using MaxEnt. Environmental variables consisted of climate variables taken from BIOCLIM, river data and soil data. The future climate data were obtained from CHELSA from CMIP6 climate models. The same variables were collected for three scenarios (SSP1‐2.6, SSP3‐7.0 and SSP5‐8.5) of shared socioeconomic pathways for the years 2050–2070 and 2080–2100. For each scenario and period, diversity maps were prepared for six species groups: all species, Lentic, Lotic, Oriental, Afrotropical and Palaearctic species.ResultsStrong declines in diversity are expected in western Turkey, the Levant and Azerbaijan, and to a lesser extent in parts of Iran and southern Central Asia. An increase is expected in eastern Turkey and at higher elevations in Central Asia with a limited increase throughout the Arabian Peninsula. In contrast to expectations, a decrease in areas with <15 species was found. Faunal composition is predicted to show strong shifts, with Palaearctic species declining and Oriental and Afrotropical species increasing. No clear difference between the trend of lentic and lotic species is found, although there are clear spatial differences in trend between these groups.Main ConclusionsClimate change will result in strong changes in diversity and distribution of dragonflies and damselflies in West and Central Asia with regional declines and increases. None of the species are predicted to go extinct based on the impact of climate change only, however, the combined impact of climate change and anthropogenic forces is likely to push some of the species to near extinction by 2100.

A Statistical Downscaling Model Based on Multiway Functional Principal Component Analysis for Southern Australia Winter Rainfall

Abstract We propose a statistical downscaling model based on multiway functional principal component analysis (FPCA) for rainfall prediction. The model mainly explains the relationship between the winter mean sea level pressure (MSLP) and rainfall in southern Australia from the perspective of functional data. In comparison with the traditional approach of feature extraction based on principal component analysis, the multiway FPCA needs fewer principal components not only to capture the most variance in MSLP but also to greatly avoid the loss of spatial information. A functional principal component (FPC) regression is further developed to simulate both current and future rainfall. The main results show that the first five leading FPCs are sufficient to capture the spatial characteristics of winter MSLP, achieving the purpose of efficient dimensionality reduction. Specifically, no more than three FPCs are required to develop the functional downscaling models for the winter rainfall over four studied regions. The functional downscaling model provides a good skill in terms of the correlation higher than 0.7 between the predictions and observations and the ratio of root-mean-square error to the climatology of winter rainfall below 20% over four regions. The developed downscaling models are further used to interpret the MSLP patterns from four CMIP5 climate models [ACCESS1.3, BCC_CSM1.1(m), CESM1(CAM5), and MPI-ESM-MR], which have been used to simulate both present-day and future climate. The resulting downscaled values based on ensemble MSLP provide 1) a closer representation of observed present-day rainfall than the raw climate model values and 2) alternative estimates of future changes in rainfall that arise from changes in MSLP.

Different GCMs yet similar outcome: predicting the habitat distribution of Shorea robusta C.F. Gaertn. in the Indian Himalayas using CMIP5 and CMIP6 climate models.

Climate change impact on the habitat distribution of umbrella species presents a critical threat to the entire regional ecosystem. This is further perilous if the species is economically important. Sal (Shorea robusta C.F. Gaertn.), a climax forest forming Central Himalayan tree species, is one of the most valuable timber species and provides several ecological services. Sal forests are under threat due to over-exploitation, habitat destruction, and climate change. Sal's poor natural regeneration and its unimodal density-diameter distribution in the region illustrate the peril to its habitat. We, modelled the current as well as future distribution of suitable sal habitats under different climate scenarios using 179 sal occurrence points and 8 bioclimatic environmental variables (non-collinear). The CMIP5-based RCP4.5 and CMIP6-based SSP245 climate models under 2041-2060 and 2061-2080 periods were used to predict the impact of climate change on sal's future potential distribution area. The niche model results predict the mean annual temperature and precipitation seasonality as the most influential sal habitat governing variables in the region. The current high suitability region for sal was 4.36% of the total geographic area, which shows a drastic decline to 1.31% and 0.07% under SSP245 for 2041-60 and 2061-80, respectively. The RCP-based models predicted more severe impact than SSP; however, both RCP and SSP models showed complete loss of high suitability regions and overall shift of species northwards in the Uttarakhand state. We could identify the current and future suitable habitats for conserving sal population through assisted regeneration and management of other regional issues.

Changes in March mean snow water equivalent since the mid-20th century and the contributing factors in reanalyses and CMIP6 climate models

Abstract. Trends in March mean snow water equivalent (SWE) in the Northern Hemisphere are attributed to changes in three main factors: total precipitation (P), fraction of precipitation as snowfall (F), and fraction of accumulated snowfall remaining on the ground (G). This trend attribution is repeated for two reanalyses (ERA5-Land from March 1951 to 2022 and MERRA2 – Modern-Era Retrospective analysis for Research and Applications, Version 2 – from 1981 to 2022) and simulations by 22 climate models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6). The results reveal a decrease in SWE in most of the Northern Hemisphere, as decreases in F and G dominate over mostly positive trends in P. However, there is spatial variability in both the magnitude and sign of these trends. There is substantial variation between the individual CMIP6 models, but the agreement between the CMIP6 multi-model mean and ERA5-Land is reasonable for both the area means and the geographical distribution of the trends from 1951 to 2022, with a spatial correlation of 0.51 for the total SWE trend. The agreement for the trends from 1981 to 2022 is worse, probably partly due to internal climate variability but also due to the overestimation of the recent warming in the CMIP6 models. Over this shorter period for which ERA5-Land can be compared with MERRA2, there are also marked trend differences between these two reanalyses. However, the SWE decreases associated with reduced snowfall fraction (F) are more consistent between the different data sets than the trends resulting from changes in P and G.

Impact of Climate Change and Consumptive Demands on the Performance of São Francisco River Reservoirs, Brazil

Climate change in the coming decades could intensify extreme events such as severe droughts. Combined with the possible increase of water demands, these changes exert a great deal of pressure on the water systems. In order to confirm this assumption, a set of scenarios was proposed in this study to consider the combined impact of climate changes and the increase in water demand on the main multiple-use reservoirs of São Francisco River, Brazil. For this purpose, five CMIP6 climate models were used, considering two greenhouse gas emissions scenarios: the SSP2-4.5 and SSP5-8.5. The affluent natural flows and regulated flows were estimated to the adopted reservoirs considering all existing, new and projected demands. The combination of scenarios indicated an increase in Potential Evapotranspiration; possible significant reduction in water availability, with a decrease in precipitation with a magnitude of −15% in the most pessimistic scenarios and a decrease in flows with a magnitude varying between −5% and −40% in the Sobradinho and Três Marias reservoirs; growth in water demand, mainly for irrigation, with annual rates of 6.80%, 7.42%, 10.99% and 9.29% for Itaparica, Sobradinho, Três Marias and Retiro Baixo, respectively; and a substantial reduction in the performance of the evaluated reservoirs, mainly for the Retiro Baixo and Itaparica reservoirs, which showed a high vulnerability index and a low sustainability index.

Skillful Seasonal Prediction of Typhoon Track Density Using Deep Learning

Tropical cyclones (TCs) seriously threaten the safety of human life and property especially when approaching a coast or making landfall. Robust, long-lead predictions are valuable for managing policy responses. However, despite decades of efforts, seasonal prediction of TCs remains a challenge. Here, we introduce a deep-learning prediction model to make skillful seasonal prediction of TC track density in the Western North Pacific (WNP) during the typhoon season, with a lead time of up to four months. To overcome the limited availability of observational data, we use TC tracks from CMIP5 and CMIP6 climate models as the training data, followed by a transfer-learning method to train a fully convolutional neural network named SeaUnet. Through the deep-learning process (i.e., heat map analysis), SeaUnet identifies physically based precursors. We show that SeaUnet has a good performance for typhoon distribution, outperforming state-of-the-art dynamic systems. The success of SeaUnet indicates its potential for operational use.

Design Rainfall Change of Rainwater Source Control Facility to Meet Future Scenarios in Beijing.

Rainwater source control facilities are essential to sponge city construction in China. Their size is determined based on historical rainfall data. However, with global warming and rapid urban development, rainfall characteristics have also changed, potentially leading to the failure of rainwater source- control facilities to manage surface water in the future. In this study, the design rainfall's change and spatial distribution are analyzed using historical (1961-2014) observation rainfall data and future (2020-2100) projection data of three CMIP6 climate models. The results show that EC-Earth3 and GFDL-ESM4 project that future design rainfall will increase. EC-Earth3 projects a significant increase, while MPI-ESM1-2 projects that the design rainfall will decrease significantly. From the perspective of space, the design rainfall isoline in Beijing has always increased from northwest to southeast. In the historical period, the difference in design rainfall in different regions has reached 19 mm, and this regional heterogeneity shows an increasing trend in the future projection of EC-Earth3 and GFDL-ESM4. The difference in design rainfall in different regions is 26.2 mm and 21.7 mm, respectively. Therefore, it is necessary to consider future rainfall changes in the design of rainwater source control facilities. The relationship curve between the volume capture ratio (VCR) of annual rainfall and design rainfall based on the rainfall data of the project site or region should be analyzed to determine the design rainfall of the rainwater source control facilities.

Impacts of soil erosion and climate change on the built heritage of the Pambamarca Fortress Complex in northern Ecuador.

The Pambamarca fortress complex in northern Ecuador is a cultural and built heritage with 18 prehispanic fortresses known as Pucaras. They are mostly located on the ridge of the Pambamarca volcano, which is severely affected by erosion. In this research, we implemented a multiscale methodology to identify sheet, rill and gully erosion in the context of climate change for the prehistoric sites. In a first phase, we coupled the Revised Universal Soil Loss Equation (RUSLE) and four CMIP6 climate models to evaluate and prioritize which Pucaras are prone to sheet and rill erosion, after comparing historical and future climate scenarios. Then, we conducted field visits to collect geophotos and soil samples for validation purposes, as well as drone flight campaigns to derive high resolution digital elevation models and identify gully erosion with the stream power index. Our erosion maps achieved an overall accuracy of 0.75 when compared with geophotos and correlated positively with soil samples sand fraction. The Pucaras evaluated with the historical climate scenario obtained erosion rates ranging between 0 and 20 ton*ha-1*yr-1. These rates also varied from -15.7% to 39.1% for four future climate change models that reported extreme conditions. In addition, after identifying and overflying six Pucaras that showed the highest erosion rates in the future climate models, we mapped their gully-prone areas that represented between 0.9% and 3.2% of their analyzed areas. The proposed methodology allowed us to observe how the design of the Pucaras and their concentric terraces have managed to reduce gully erosion, but also to notice the pressures they suffer due to their susceptibility to erosion, anthropic pressures and climate change. To address this, we suggest management strategies to guide the protection of this cultural and built heritage landscapes.

CLIMBra - Climate Change Dataset for Brazil

General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.