Indicators Of Climate Change Research Articles

WACCM6 Projections of Polar Mesospheric Cloud Abundance Over the 21st Century

AbstractPolar mesospheric clouds (PMC), or noctilucent clouds, can be observed over high latitudes with the naked eye from the ground or from space near the summer solstice. PMC are considered a direct and sensitive indicator of climate change and have been reported to appear more frequently in recent decades. How PMC will change in the future under the influence of natural variability and anthropogenic forcing is uncertain. In this study, we utilize model output from the Whole Atmosphere Community Climate Model under several shared socioeconomic pathway (SSP) scenarios and input the water vapor, temperature, and pressure information into a 0‐d PMC model to project the trend and variation of PMC over the 21st century, and their relationship to future changes of temperature, water vapor, and the solar cycle. The 0‐d model calculations indicate that PMC ice water content (IWC) will increase and PMC will extend to lower latitudes under high SSP scenarios. Under these scenarios, more mesospheric water vapor leads to an increased IWC of PMC over the polar region, and colder mesopause temperature leads to more PMC over the mid‐latitudes. There is a significant anti‐correlation between the solar cycle and PMC IWC over the 21st century, but the anti‐correlation is not always significant on the decadal scale. Finally, methane oxidation in the stratosphere and water vapor entering from the troposphere are both responsible for future changes in mesospheric water vapor and thus PMC.

Climate change and agriculture nexus in Bangladesh: Evidence from ARDL and ECM techniques

The erratic weather puts farming households of Bangladesh at high production risk with significant consequences on food production, income, and livelihood. This study attempts to find the effect of various climate change indicators on agriculture in Bangladesh over the period 1980–2014. The study used the ARDL bounds testing approach to assess the long-run associations and the Granger causality test to determine the causal relationships between the regressors and dependent variables. The outcomes revealed that the first lag of agricultural value-added, second lag of carbon emissions, and average rainfall have a positive impact while the first lag of carbon has negative and significant impacts on agricultural production in the long run; in the short run-past realizations of carbon emission have a negative and significant impact on agricultural value-added. Additionally, the results show a unidirectional causality from carbon emission to agricultural output, agricultural output to average rainfall, and agricultural output to energy consumption. The study fills the gap in the climate change literature by applying the ARDL method to establish the nexus between climate change and agricultural output in Bangladesh.

Regional economic costs of climate change: An interdisciplinary impact assessment for Upper Austria

Region-specific meteorological data show that Upper Austria will mainly be affected by increasing temperatures (up to +2.7 °C in 2050) and decreasing precipitation (up to - 27 mm in 2050). Using an interdisciplinary framework, we derive climatic developments and quantify the resulting direct sectoral and macroeconomic impacts for Upper Austria. Based on a set of climate change indicators, sectoral damages are monetized for selected impact chains in forestry, health, agriculture, space heating and cooling, and winter tourism. These damage costs are used as input for ex-ante simulations to quantify the macroeconomic impacts in 2022–2050. The results show an annual decline in gross regional product, accompanied by an annual decline in employment. This study provides a basis for decision making in Upper Austria, as well as in regions with comparable geographical, economic or demographic structures, and highlights the importance of region-specific climate change adaptation strategies.

Warming-dominated climate change impacts on soil organic carbon fractions and aggregate stability in Mollisols

Mollisols contain high amounts of soil organic carbon (SOC), which is highly susceptible to climate change; thus, climate change could indirectly influence soil aggregate stability, but the dominant factor affecting aggregate stability remains controversial. Here, a soil transplanting test from high-latitude to low-latitude locations was initiated in 2004 to investigate the influences of warming-dominated climate change (approximately 3–4.7 ℃) on the quantity and molecular composition of OC fractions in surface (0–20 cm) soils, aggregate stability changes and underlying mechanisms. Different initial soil organic matter (SOM) contents of 50.6 g kg−1 (SOM5), 58.8 g kg−1 (SOM6), and 108.9 g kg−1 (SOM11) were established in situ soils and in transplanted soils to simulate warming. The 15-year warming-dominated climate change presented no noticeable change in the SOC content in the lower SOM Mollisols (SOM5 and SOM6) but increased the SOC content by 13.3% in the higher SOM Mollisol (SOM11). In terms of labile OC fractions, warming-dominated climate change significantly increased the dissolved organic carbon (DOC) content by 20.1%–47.7% but reduced the easily oxidizable organic carbon (EOC) and microbial biomass carbon (MBC) contents by 22.1%–33.6%. Irrespective of any treatment, warming-dominated climate change decreased soil aggregate stability, as evidenced by the reduction in mean weight diameter (MWD) and geometric mean diameter (GMD) of 41.7%–79.3% and an increase in fractal dimension (D) of 28.6%–58.5%. For hierarchically organized soil aggregates, warming-dominated climate change increased the proportion and OC content of particulate organic matter inside free microaggregates (Fm-POM) as well as nonaggregated silt + clay-sized organic matter (nA-MOM). However, climate change decreased the proportion and OC content of silt + clay-sized fractions inside microaggregates within macroaggregates (mM-MOM). Of importance, warming-dominated climate change increased the amount of carbohydrates and decreased the amount of lignin in the mM-MOM, Fm-POM, and nA-MOM fractions. Therefore, we speculated that chemical protection by mineral association within macroaggregates and nA-MOM fractions and physical protection by the occlusion of POM within free microaggregates might be the primary mechanisms for SOC stabilization under long-term climate change in Mollisols. The long-term warming-dominated climate change results demonstrated a positive effect on SOC content in higher SOM Mollisols, DOC, carbohydrate C, free microaggregate-associated C and nonaggregated silt + clay-sized associated C but exhibited a negative effect on EOC, MBC, lignin C, silt + clay-sized inside microaggregates within macroaggregates C and aggregate stability. These variables all contributed to the reduction in soil aggregate stability and might act as sensitive indicators of warming-dominated climate change in Mollisols, which in turn affect farmland ecosystem C fluxes in response to further climate change.

Built environment transformation in Nigeria: the effects of a regenerative framework

ABSTRACT The promise of a healthier, more comfortable, and more productive way of life has fueled a rapid technological transition, and a regenerative built environment has emerged as the tagline to denote the recent sustainable development. In the built environment, the regenerative paradigm has emerged as a transformative approach that goes beyond mere sustainability, aiming to restore, renew, and enhance the ecosystems affected by human activities. However, the concept of the regenerative paradigm and its potential to foster sustainable development has been understudied in recent time. Therefore, this paper explores how we can transform the built environment in the face of the present impacts of climate change using a new regenerative paradigm concept. The objectives of the study are: (i) to explore the predictors of climate change, (ii) to determine the negative impacts of environmental issues on inhabitants’ health, and (iii) to explore adaptive climate change strategies for Nigeria’s regenerative built environment. The study sample consisted of 235 stratified respondents’ opinions from within the built environment in southwestern Nigeria collected via a self-administered questionnaire. The collected quantitative data was analysed using SPSS (version 22) logistic regression analysis. The major results of the analysis revealed: (i) the ten most important predictors of climate change indicators, (ii) the existence of negative consequences of the impacts of climate change on inhabitants’ health in southwestern regions of Nigeria, and (iii) a significant (p ≤ 0.05) in all regenerative factors: planting native species has the highest β coefficient of 0.499, followed by the biophilic approach (0.494), the establishment of a city’s tree canopy (0.467), the creation of a green functional green space (0.436), the use of smart landscaping techniques (0.388), and the development of a healthy watershed (0.314). This indicates that to have a regenerative built environment it is essential to create a functional green space, plant native species, establish a city’s tree canopy, create a healthy watershed, and render a biophilic approach. The study’s recommendations include urgent action to integrate climate change interventions into the decision-making processes, initiatives, and development plans of the Nigerian government. This integration should prioritize sustainable practices within the built environment, considering the regenerative paradigm’s potential to address climate change impact effectively.

Advancing perspectives in geomorphology – An apotheosis to sustainable natural spaces and eudaemonic citizenry in the 21st century

Geomorphology deals with analysis of geo features that occur in a spatial array and develop over a temporal frame. In modern times investigations in geomorphology have progressed with the application of concrete theories of pure geomorphology and techniques of applied geomorphology for modelling future interpolations and projections affecting the Earth’s natural systems. The scale and temporal frame of landform alteration by climate-induced processes (flowing water, glaciers, wind and marine processes) are important indicators of climate change. The integration of applied studies in natural processes and their probable impact on social milieu now requires greater accuracy. The subfields of geomorphology all have now developed multidisciplinary nature and effectively are being integrated in investigations with trans-disciplinary scope. The rapid progress in digital mapping capacities helps to monitor and assess geomorphic processes and estimate their impact on citizenry with precision. Digitization in cartography facilitates and mitigates disaster management in an integrated manner. Adequate planning and budgetary allocations for disaster management by planners and policy makers are simplified. This paper discusses the recent development of latest sub-disciplines in geomorphology, discussing how broad its scope has developed to cater to societal welfare that aims at millennium sustainable development and contented citizenry.

The impact of climate change on international trade: A gravity model estimation

AbstractThe aim of this article is to analyse the impact of climate change on bilateral trade flows. To this end, we estimate a theory‐grounded gravity model using bilateral trade flows on a sample of 67 countries over the period from 1986 to 2016. We use temperatures and extreme weather events (and their consequences) as indicators of climate change. In general, we find that international trade flows are less affected by the evolution of temperatures than domestic ones, while the opposite occurs with extreme weather events. However, there is a clear heterogeneity across countries and types of events. In particular, the results suggest that biological events, such as (epidemics and insect infestation), extreme temperatures, storms and landslides have a negative impact on international flows relative to internal ones. Interestingly, the influence of China and Japan on the global result is decisive, especially for storms in the first case and extreme temperatures in the second. Finally, our General Equilibrium estimations suggest a more detrimental impact of insect infestation and extreme temperatures on welfare than the rest of events.

Quantitatively mapping the research status and trends of vegetation responses to climate change with bibliometric analysis

PurposeVegetation is a typical sensitive indicator of climate change, and therefore provides theoretical and valuable information for addressing issues arising from climate change including improving soil ecosystem services. Exploring how vegetation responses to climate change has become one of major hotspots of research. However, few scholars have performed bibliometric analyses of this field. This study investigated the current research activities and the trend developments of vegetation responses to climate change.Materials and methodsWe conducted a quantitative bibliometric analysis of 2,310 publications on vegetation responses to climate change from 1991 to 2021 retrieved in the Web of Science Core Collection. The analysis comprised significant journals, disciplines, and scholars, as well as partnerships between countries and institutions, keyword co-occurrence and burst analysis. The bibliometric analysis tools, Histcite, Vosviewer, CiteSpace software, and R (Bibliometrix package), were applied.Results and discussionThe related publications on vegetation responses to climate change had been increasing exponentially in the past 30 years and its total global cited score reached its peak in 2010. The USA and China were the leading countries, with the Chinese Academy of Sciences having the highest number of publications and citations. The scholars who had the most citations were Allen CD, Bresears DD, and Running SW. Six research clusters were generated by keywords co-occurrence analysis, including impact, response, CO2, growth, climate change, and vegetation. These clusters represented the current research topics that highlighted the responses of vegetation to climate change, the manifestation of its impact, and coping strategies. In future research on vegetation, the emphasis is expected to be placed on “human activities” and “N2O emission”.ConclusionThis study has performed a comprehensive and systematic and quantitative analysis of the publications on the responses of vegetation to climate change. The results reveal the characteristics, development patterns, and research trends of studies on vegetation activity in response to climate change, which sheds new insights into understanding the relationship between soil and climate.

Inventory methodology of periglacial lakes in Spitzbergen (Svalbard)

The paper discusses periglacial lakes, which were formed after the Little Ice Age due to the reduction of Spitzbergen (Svalbard) glaciation. A method for inventorying the periglacial lakes in Spitzbergen (Svalbard) is proposed based on the Norwegian Polar Institute cartographic materials and remote sensing methods. Such lakes have been formed due to deglaciation in Svalbard since the beginning of the 20th century after the Little Ice Age. Three morphological features of the periglacial lake were used as selection criteria: the lake is in contact with the glacier, the lake is located within the terminal moraine or borders on it, each of which is sufficient.As a result of the inventory, 705 periglacial lakes have been found in Spitzbergen (Svalbard). The largest number of lakes is located in the northeastern part of the archipelago, where the relief is poorly dissected. The largest clusters of lakes are found on the moraines of mountain-valley glaciers located on the warmer western coasts. The periglacial lakes were divided into 5 types: glacier-dammed lakes (19 %), lakes in contact with the glacier front (30 %), thermokarst lakes on the moraine (27 %), moraine-dammed lakes (15 %) and lakes in contact with the terminal moraine (9 %). The most numerous were periglacial lakes adjoining the glacier (about 50 %). Their total area is 162 km2 and accounts for 87 % of the total area of all periglacial lakes. The other half of the lakes have formed shores, and their transformation is likely to be much slower in the future. The quantitative ratio of lake types testifies to the active phase of the process of lake expansion in the archipelago. This ratio of lakes by groups can be a clear indicator of climate change in the archipelago.

Recent changes in the snow cover characteristics in Poland

AbstractSnow cover (SC) is a great indicator of climate change. It is highly related to temperature. Since the Polish climate faces warmer conditions, changes in the wintertime precipitation phase are observed. More frequent rainfall instead of snowfall is noticed at the end of the 20th and beginning of the 21st centuries. This work presents the spatial distribution, its changes, and variabilities of selected parameters that described snow cover in Poland from 1966/1967 to 2020/2021. The snow characteristics used in the study comprise of SC duration (SCD), the first and last day with SC in the season (SCbeg and SCend), the potential duration of SC season (PSCD), SC stability (SCS), average, maximum and accumulated SC depth (HS, HSmax and AHS). The changes in snow cover waves were analysed. Generally, the Polish climate is mild in the west, where the snow cover is finer and occurs relatively rarely and becomes more continental toward the north‐east, where the snow cover has a better condition to accumulate and preserve. The southern parts of the country are covered by mountain ranges of the Carpathians and Sudetes, where the snow cover remains the longest. The spatial distribution of the coefficient of variability is reversely proportional to SC characteristics—the highest in the north‐east (especially for the HS, HSmax and AHS). The most significant changes in SC are related to a decrease in SCD (5–7 days/decade), HSmax (1–2 cm/decade) and AHS (30–60 cm/decade). At the same time, the snow cover season becomes considerably shorter (especially in the western and central parts—about 10 days/decade). The trends in SCS are not yet significant in most of the country. This study reveals that the snow cover in Poland is under constant change, and the negative trends, which were hardly or not visible at the end of the 20th century, in the last decades have become statistically significant in a greater number of Polish stations.

Spatio-temporal compounding of connected extreme events: Projection and hotspot identification

In general, the impact of two different connected extreme events is noticed on the same duration and spatial area. However, the connected extreme events can have footprint over different temporal and spatial scales. Thus, this article analyses the connected extreme events over India using the spatio-temporal compounding technique to understand the impact at different temporal and spatial scales. This approach is applied to analyse the historical and future connected extreme events. In the present study, coincident heat waves and droughts (Event C1), coincident heat waves and extreme precipitation (Event C2) are considered as connected extreme events. The future events are investigated using the suitable global climate models (GCMs) projections under three climate change scenarios (Shared Socioeconomic Pathways (SSP) 2–4.5, SSP3-7.0, and SSP5-8.5). The suitable GCMs are identified with the help of compromise programming. Subsequently, the hotspot regions are identified applying the Regional Climate Change Index (RCCI) method. The outcomes from the study suggest that with increasing temporal compounding, the mean duration of extreme events also increases. Highest increase in mean duration is observed for Event C1 over PI (Peninsular India), WCI (West Central India), and some parts of CNI (Central Northeast India) regions. The regions with high magnitude of duration have low magnitude of occurrence. The duration of Event C1 is likely to increase with respect to climate change scenarios and temporal compounding, especially in the PI region and some parts of WCI. However, there is insignificant change in the duration of Event C2. The PI region identified as the most vulnerable region followed by WCI and HR regions. The highest percentage of area under the emerging hotspot category is noticed under SSP5-8.5 climate change scenario.

Pelagic ciliate (Ciliophora) communities in the Southern Ocean: Bioindicator to water mass, habitat suitability classification and potential response to global warming

Although the pelagic ciliate (microzooplankton) play important role in carbon flux of marine ecosystems, their ecological function, habitat suitability classification and response to global warming were poorly documented in the Southern Ocean (SO). During summer of 2020/2021, ciliate (including aloricate ciliates and tintinnids) communities in waters from surface to 1000 m were investigated in the SO. Rapid attenuation of ciliate abundance and biomass occurred at 100 m depth in most stations. According to distinctive hydrographic features, five water masses were identified in the SO with unique ciliate community structure. Aloricate ciliate small size-fraction (10–20 μm) was dominant in water masses deeper than 200 m. Tintinnids showed distinctive regional and vertical distribution characteristics, and their habitat suitability characterized by the Bio-index exhibiting different optimum survival zones. Variations in tintinnid habitat can be regarded as indicators for future SO climate change. Correlation between environment factors and biota revealed that depth, Chl a and dissolved oxygen were main factors determining ciliate composition. Combining tendency of phytoplankton and meso-/macro-zooplankton to rapid global warming, ciliates may become smaller and more prominent in the future SO marine pelagic ecosystem. Our study has laid a foundation for recognizing the ciliate community and reinforcing the ideas about tintinnid indication for the change of future SO marine ecosystem.

Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery

Glaciers serve as sensitive indicators of climate change, making accurate glacier boundary delineation crucial for understanding their response to environmental and local factors. However, traditional semi-automatic remote sensing methods for glacier extraction lack precision and fail to fully leverage multi-source data. In this study, we propose a Transformer-based deep learning approach to address these limitations. Our method employs a U-Net architecture with a Local-Global Transformer (LGT) encoder and multiple Local-Global CNN Blocks (LGCB) in the decoder. The model design aims to integrate both global and local information. Training data for the model were generated using Sentinel-1 Synthetic Aperture Radar (SAR) data, Sentinel-2 multispectral data, High Mountain Asia (HMA) Digital Elevation Model (DEM), and Shuttle Radar Topography Mission(SRTM) DEM. The ground truth was obtained for a glaciated area of 1498.06 km2 in the Qilian mountains using classic band ratio and manual delineation based on 2 m resolution GaoFen (GF) imagery. A series of experiments including the comparison between different models, model modules and data combinations were conducted to evaluate the model accuracy. The best overall accuracy achieved was 0.972. Additionally, our findings highlight the significant contribution of Sentinel-2 data to glacier extraction.

Greenland ice sheet rainfall climatology, extremes and atmospheric river rapids

AbstractGreenland rainfall has come into focus as a climate change indicator and from a variety of emerging cryospheric impacts. This study first evaluates rainfall in five state‐of‐the‐art numerical prediction systems (NPSs) (CARRA, ERA5, NHM‐SMAP, RACMO, MAR) using in situ rainfall data from two regions spanning from land onto the ice sheet. The new EU Copernicus Climate Change Service (C3S) Arctic Regional ReAnalysis (CARRA), with a relatively fine (2.5 km) horizontal grid spacing and extensive within‐model‐domain observational initialization, has the lowest average bias and highest explained variance relative to the field data. ERA5 inland wet bias versus CARRA is consistent with the field data and other research and is presumably due to more ERA5 topographic smoothing. A CARRA climatology 1991–2021 has rainfall increasing by more than one‐third for the ice sheet and its peripheral ice masses. CARRA and in situ data illuminate extreme (above 300 mm per day) local rainfall episodes. A detailed examination CARRA data reveals the interplay of mass conservation that splits flow around southern Greenland and condensational buoyancy generation that maintains along‐flow updraft ‘rapids’ 2 km above sea level, which produce rain bands within an atmospheric river interacting with Greenland. CARRA resolves gravity wave oscillations that initiate as a result of buoyancy offshore, which then amplify from terrain‐forced uplift. In a detailed case study, CARRA resolves orographic intensification of rainfall by up to a factor of four, which is consistent with the field data.

Climate changes remarks in a glacial catchment within Southern Rhaetian Alps: the Fumo Valley case study (Adamello Group, Northern Italy)

In this study, glacier retreat from the end of the Little Ice Age to present time and climatic variations on recent time scale were assessed in the Fumo Valley, which is a small glacial catchment located in the Adamello Group (Southern Rhaetian Alps, Italy). The study area has a high scientific and cultural value as part of the UNESCO Adamello-Brenta Geopark and provides several ecosystem and socio- economic services. Therefore, the assessment of climate change impacts at local scale is essential for a sustainable watershed management, as well as for conservation strategies.A present-state glacier inventory was elaborated by means of remote sensing using Sentinel-2 satellite images. Then, it was compared to previous published glacier inventories in order to assess numerical and geometric surface variations. Also, two temperature data series related to different thirty-year periods (i.e., 1971-2000 and 1991-2020) were analysed to assess air temperature variations. The results highlight a total surface loss of 5.99 km2 (-70.9%) between 1850 and 1958. Between 1958 and 2022 a total area reduction of 0.87 km2 (-35.5%) was observed, which is comparable to other studies carried out at national scale. However, a minor progression phase was observed between 1958 and 1981 (+76.3%), which is partially attributable to the glacial advance episode that occurred between the 1970s and 1980s in the Italian Alps. After, total glacier area declined by 63.4% (1981-2022), with an accelerated regression between 2015 and 2022 (i.e., higher annual rate of variation, formation of ice-contact lakes, tongue separation, and severe glacier fragmentation). The mean annual air temperature rose of 0.6°C, with a greater variation in mean annual maximum temperature (+1°C) than minimum temperature (+0.1°C), and an average temperature increase higher in spring and summer than in autumn and winter. These results can be interpreted as truthful indicators of climate change impacts occurring at local scale, highlighting the vulnerability of the study area with respect to the predicted future climatic scenarios.

The Variability of Pan Evaporation over China during 1961–2020

Abstract The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (Epan) is an important indicator of climate change; however, current research on Epan has focused on its change in mean rather than its variability. The variability of monthly Epan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in Epan variance between space and time. The Epan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in Epan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of Epan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in Epan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring. Significance Statement The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice.

Potential impact of stratospheric aerosol geoengineering on projected temperature and precipitation extremes in South Africa

Stratospheric aerosol injection (SAI) is the theoretical deployment of sulphate particles into the stratosphere to reflect incoming solar radiation and trigger a cooling impact at the Earth’s surface. This study assessed the potential impact of SAI geoengineering on temperature and precipitation extremes over South Africa (SAF) and its climatic zones in the future (2075–2095) using simulations from the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) project. We analyse three different experiments from the GLENS project, each of which simulate stratospheric SO2 injection under the representative concentration pathway 8.5 (RCP8.5) emissions scenario: (i) tropical injection around 22.8–25 km altitude (GLENS), (ii) tropical injection around 1 km above the tropopause (GLENS_low), and (iii) injection near the equator around 20–25 km (GLENS_eq). The study used a set of the Expert Team on Climate Change Detection and Indices describing temperature and rainfall extremes to assess the impact of the three SAI experiments on extreme weather in the future over SAF. The results of this study indicate that, relative to the baseline period (2010–2030), all three SAI experiments are mostly over-effective in offsetting the projected RCP8.5 increase in the frequency of hot (up to −60%) and decrease (up to +10%) in cold temperature extremes over SAF and its climatic zones. These findings suggest that SAI could cause over-cooling in SAF. However, SAI impact on precipitation extremes is less linear and varies across the country’s climatic zones. For example, SAI could reinforce the projected decrease in precipitation extremes across most of SAF, although it could exacerbate heavy precipitation over the KwaZulu-Natal Coast. These findings are consistent across SAI experiments except in magnitude, as GLENS_eq and GLENS_low could cause larger decreases in precipitation extremes than GLENS. These findings imply that SAI could alleviate heat stress on human health, agriculture, and vulnerable communities while simultaneously decreasing infrastructure and crops’ vulnerability to flooding. It is, however, essential to interpret these findings cautiously as they are specific to the SAI experiments and modelling settings considered in the GLENS project.

Variations in Global Soil Moisture During the Past Decades: Climate or Human Causes?

AbstractAs a sensitive climate change indicator, global multi‐depth soil moisture (SM) has undergone great variation due to warming trends during the past decades. Here, we investigate the evolutionary pattern of SM and then carry out an attribution analysis from climate and human perspectives. The results reveal an unbalanced surface and rootzone variation trend during 1980–2020. The surface soil had approximately equal proportions of drying and wetting. Nonetheless, the percentage of the wetting region is significantly higher than that of the drying region for rootzone soil. The significantly drying region is mainly distributed in habitable areas, while the remarkably wetting region is usually located in the harsh climate and tropical humid zones. According to the attribution analysis using the Feature Importance Index of the Random Forest model, Pearson correlation coefficient, Maximum information coefficient, Spearman rank correlation coefficient, and Granger causality with 99% statistical significance, both climatic and human factors presented significant impacts on SM. Specifically, air temperature and evaporation are thought to be the primary climatic factors affecting the seasonal and long‐term variability of SM, respectively. Irrigation water withdrawal is regarded as the dominant driving force of SM variation among six human water withdrawals, and domestic and electricity water withdrawals play indispensable roles in SM fluctuation. Additionally, SM also provides profound feedbacks on climatic factors and human water withdrawals. The global spatial‐temporal SM variation along with warming temperatures could seriously risk food security and sustainable development, which needs to be addressed.

Time of emergence in climate extremes corresponding to Köppen-Geiger classification

Extreme climate events can pose huge threats to human safety and industrial and agricultural production. Therefore, investigating the time of emergence (TOE) of climate extremes is essential to policymakers, especially residents suffering from related climate disasters. However, there is still a lack of work focused on the TOE of climate extremes over the whole global land and aggregated climate subtypes. Here, we employed phase 5 of the Coupled Model Intercomparison Project (CMIP5) multi-models to calculate the TOE of 27 widely used Expert Team for Climate Change Detection and Indices (ETCCDI) indices for the sake of analyzing the intensity, frequency, and duration of extreme temperature and extreme precipitation characteristics comprehensively based on Köppen-Geiger climate classification. We found the temperature indices change prior to the precipitation indices in general, and are more uniformly in spatial distributions of TOE. Regionally, the warming and wetting trends are obvious in Tropical areas. Arid areas are likely to witness tiny changes for most precipitation indices. Temperate areas are the most similar climate subtype to the mean state of the global land. The substantially earlier TOE of precipitation indices in Continental and Polar areas highlights the need for an early warning system. The findings of this study will help policymakers take targeted preventive measures to deal with specific extreme events in an adaptation to local conditions.

Assessing marine heat waves in the Mediterranean Sea: a comparison of fixed and moving baseline methods

The study of marine heat waves as extreme temperature events has a wide range of applications, from a gauge for ecological and socioeconomic impact to a climate change indicator. Various definitions of marine heat waves as extreme sea temperature events exist to account for its broad applicability, with statistical definitions based on percentile based thresholds being widespread in its use. Using satellite and model data of the Mediterranean Sea, we analyze the statistical implications of choosing baseline climatological periods for threshold delineation, which are either fixed in the past or shifted in time. We show that in the context of a warming Mediterranean Sea, using a fixed baseline leads to a saturation of marine heat wave days that compromises the significance of this marine indicator, with 90% of climate models analyzed predicting an average above 189 marine heat wave days per year by 2050 even for the lowest emission scenario. We argue that only with a moving baseline, can we reach a definition for marine heat waves which yield consistently rare extreme events.