Projecting May Tornado Outbreak Patterns in a Warming Climate

Crop rotation has been widely used to enhance crop yields and mitigate adverse climate impacts. The existing research predominantly focuses on the impacts of crop rotation under growing season (GS) climates, neglecting the influences of non-GS (NGS) climates on agroecosystems. This oversight limits our understanding of the comprehensive climatic impacts on crop rotation and, consequently, our ability to devise effective adaptation strategies in response to climate warming. In this study, we examine the impacts of both GS and NGS climate conditions on the yield effect of the preceding crop in corn-soybean rotation systems from 1999 to 2018 in the US Midwest. Using causal forest analysis, we estimate that crop rotation increases corn and soybean yields by 0.96 and 0.22 t/ha on average, respectively. We then employ statistical models to indicate that increasing temperatures and rainfall in the NGS reduce corn rotation benefits, while warming GS enhances rotation benefits for soybeans. By 2051-2070, we project that warming climates will reduce corn rotation benefits by 6.74% under Shared Socioeconomic Pathway (SSP) 1-2.6 and 17.18% under SSP 5-8.5. For soybeans, warming climates are expected to increase rotation benefits by 8.36% under SSP 1-2.6 and 13.83% under SSP 5-8.5. Despite these diverse climate impacts on both crops, increasing crop rotation could still improve county-average yields, as neither corn nor soybean was fully rotated. If we project that all continuous corn and continuous soybeans are rotated by 2051-2070, county-average corn yields will increase by 0.265 t/ha under SSP 1-2.6 and 0.164 t/ha under SSP 5-8.5, while county-average soybean yields will gain 0.064 t/ha under SSP 1-2.6 and 0.076 t/ha under SSP 5-8.5. These findings highlight the effectiveness of crop rotation in the face of warming NGS and GS in the future and can help evaluate opportunities for adaptation.

<strong class="journal-contentHeaderColor">Abstract.</strong> Based on Nationally-Determined Contributions concurrent with Shared Socio-economic Pathway (SSP) 2&ndash;4.5, the IPCC predicts global warming between 2.1&ndash;3.5&deg;C (very likely range 10th&ndash;90th percentile) by 2100 AD. However, global average temperature is a poor indicator of regional warming and Global Climate Models (GCMs) require validation with instrumental or proxy data from geological archives to assess their ability to simulate regional ocean and atmospheric circulation, and thus, to evaluate their performance for regional climate projections. The Southwest Pacific is a region that performs poorly when GCMs are evaluated against instrumental observations. The New Zealand Earth System Model (NZESM) was developed from the United Kingdom Earth System Model (UKESM) to better understand Southwest Pacific response to global change, by including a nested ocean grid in the Southwest Pacific with 80 % greater horizontal resolution than the global-scale host. Here, we reconstruct region Southwest Pacific sea surface temperature (SST) for the mid-Pliocene Warm Period (mPWP; 3.3&ndash;3.0), which has been widely considered a past analogue with an equilibrium surface temperature response of +3 &deg;C to an atmospheric CO₂ concentration of ~350&ndash;400 ppm, to assess the warming distribution in the Southwest Pacific. This study presents proxy SSTs from seven deep sea sediment cores distributed across the Southwest Pacific. Our reconstructed SSTs are derived from molecular biomarkers preserved in the sediment &ndash; alkenones (i.e., U₃₇<em>^K'</em> index) and isoprenoid glycerol dialkyl glycerol tetraethers (i.e., TEX₈₆ index) and are compared with SSTs reconstructed from the Last Interglacial (125 ka), Pliocene Model Intercomparison Project (PlioMIP) outputs and transient climate model projections (NZESM and UKESM) of low to high range SSPs for 2090&ndash;2099 AD. Mean interglacial equilibrium SSTs during the mPWP for the Southwest pacific sites, were on average, 4.2 &deg;C (1.8&ndash;6.1 &deg;C likely range) above pre-industrial and show good agreement with model outputs from NZESM and UKESM under mid-range SSP 2&ndash;4.6 conditions. These results highlight that not only is the mPWP an appropriate analogue when considering future temperature change in the centuries to come, but also demonstrate that the Southwest Pacific region will experience warming that exceeds that of the global mean if atmospheric CO₂ remains above 350 ppm.

This study aims to investigate the historical (1951–2024) and future projected (2026–2099) climate trends and change points in the Vellar River Basin under Shared Socioeconomic Pathways (SSP) using time series observations and downscaled Global Climate Model (GCM). Trends of rainfall and temperature were assessed based on the Mann–Kendall test. The change points detection were carried using Pettitt’s test, Standard Normal Homogeneity test, Buishand's range test and Von Neuman ratio test. The results revealed that historical rainfall found a declining trend seasonally as well as annually, with significant change points at 1979, 1981, and 1988. Future projections showed decreasing trend in rainfall, with notable change points at 2058, 2067, and 2084. The station Virudhachalam during Kharif, Vazhapadi and Karumandurai during Rabi and Annual period recorded more significant decreasing trend. The result of historical and future temperature projection displayed a significant upward trend. The historical minimum and maximum temperatures increased from 0.44 °C to 0.88 °C and 1.11 °C to 1.7 °C, respectively with change points for minimum temperature at 1996 and maximum temperature at 1979. The future predicts of temperature recorded an increasing trend of 1.5 °C, 2.0 °C, and 3.0 °C for maximum temperatures and 0.9 °C, 1.2 °C, and 1.5 °C for minimum temperatures under SSP 2.6, SSP 4.5, and SSP 8.5 scenario, respectively. Change points in maximum temperature are expected in the 2050s and 2060 for SSP 2.6 and SSP 4.5. Whereas SSP 8.5 anticipates at later 2080s, indicating a two-stage warming pattern with an early gradual increase followed by rapid rise towards the end of the century. These findings concluded by recommending the policy makers to develop climate adaptation strategies for agricultural and water management to mitigate the potential impacts of future climate condition.

Fire plays a critical role in both the formation and degradation of ecosystems; however, there are still significant uncertainties in the estimation of burned areas (BAs). This study systematically evaluated the performance of ten global climate models (GCMs) in simulating global and regional BA during a historical period (1997–2014) using the Global Fire Emissions Database version 4.1s (GFED4s) satellite fire product. Then, six of the best models were combined using Bayesian Model Averaging (BMA) to predict future BA under three Shared Socioeconomic Pathways (SSPs). The results show that the NorESM2-LM model excelled in simulating both global annual and monthly BA among the GCMs. GFDL-ESM4 and UKESM1-0-LL of the GCMs had the highest Pearson’s correlation coefficient (PCC), but they also exhibited the most significant overestimation of monthly BA variations. The BA fraction (BAF) for GCMs was over 90% for small fires (&lt;1%). For small fires (2~10%), GFDL-ESM4(j) and UKESM1-0-LL(k) outperformed the other models. For medium fires (10–50%), CESM2-WACCM-FV2(e) was closest to GFED4s. There were large biases for all models for large fires (&gt;50%). After evaluation and screening, six models (CESM2-WACCM-FV2, NorESM2-LM, CMCC-ESM2, CMCC-CM2-SR5, GFDL-ESM4, and UKESM1-0-LL) were selected for weighting in an optimal ensemble simulation using BMA. Based on the optimal ensemble, future projections indicated a continuous upward trend across all three SSPs from 2015 to 2100, except for a slight decrease in SSP126 between 2071 and 2100. It was found that as the emission scenarios intensify, the area experiencing a significant increase in BA will expand considerably in the future, with a generally high level of reliability in these projections across most regions. This study is crucial for understanding the impact of climate change on wildfires and for informing fire management policies in fire-prone areas in the future.

Socioeconomic development and natural ecosystems of China are strongly affected by droughts. The socioeconomic risk of droughts in China should be quantified under climate change. This study assessed drought impacts on population and gross domestic product (GDP) under the 2.0°C‐warmer climate scenario, by implementing multiple general circulation model (GCM) simulations under the representative concentration pathway (RCP). Using the standardized precipitation evapotranspiration index (SPEI), we calculated drought frequencies in the base period (1986–2005) and 2.0°C‐warmer climate (2040–2059 in RCP4.5). Then, population and GDP exposures were evaluated by combining the drought frequency with population and GDP simulations under a shared socioeconomic pathway (SSP). Droughts were likely to occur more frequently under the 2°C‐warmer climate, especially for inland areas of northern China (i.e., Xinjiang, Xizang, Qinghai, Ningxia, Shaanxi, Gansu provinces), consisting mainly of mild and moderate droughts. Population exposure to drought was projected at 4.94 ± 0.36 × 109 person‐months, around 2% increase over the base period value. GDP exposure would increase significantly to 2.08 ± 0.15 × 1014 PPP $‐months, approximately 14‐fold over the base period. Probability analysis showed nearly 50% of the grid cells of China's entire territory undergoing more than an 11‐fold increase in GDP exposure. Our findings indicated the climate and GDP effects were primary contributors, respectively, to the changed population and GDP exposures to drought. Population and GDP exposures were distributed unequally across the country, having a large impact in the east where both population and economic activity are highly agglomerated. Our results reveal an urgent need to design and implement effective adaptation measures to overcome droughts, especially in eastern China.

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).

The impact of climate change on the Krishna River Basin (KRB) is significant due to the semi-arid nature of the basin. Herein, 21 global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were examined to simulate the historical monthly precipitation over the 1951–2014 period in the KRB. The symmetrical uncertainty (SU) method and the multi-criteria decision method (MCDM) were employed to select the suitable GCMs for projecting possible changes in precipitation over the KRB. The biases in the climate projections were removed by using the empirical quantile mapping method. The reliability ensemble averaging (REA) method was used to generate the multi-model ensemble (MME) mean of projections and to analyse the spatio-temporal changes of precipitation under different shared socioeconomic pathways (SSPs). BCC-CSM2-MR, IPSL-CM6A-LR, MIROC6, INM-CM5-0, and MPI-ESM1-2-HR were found to be the most suitable GCMs for the KRB. The MME mean of the chosen GCMs showed significant changes in precipitation projection that occurs for a far future period (2071–2100) over the KRB. The projection changes of precipitation range from −36.72 to 83.05% and −37.68 to 95.75% for the annual and monsoon periods, respectively, for various SSPs. Monsoon climate projections show higher changes compared with the annual climate projections, which reveals that precipitation concentration is more during the monsoon period over the KRB.

This study analyzes the variations in soil erosion rate in a tropical catchment in India using the empirical Revised Universal Soil Loss Equation (RUSLE) model integrated with climate change scenarios from an ensemble of global climate models (GCMs) included in the NEX Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 dataset for Shared Socioeconomic Pathways (SSP) 126 and SSP 585. A set of seven GCMs are initially selected. Based on their ability to simulate the rainfall for the current scenario, the PROMETHEE-II method is used to rank the GCMs and the top four best-performing models are used for further analysis. Soil erosion rates projected for the future climate scenarios are compared with the current scenario. In the near future and in the middle of the century, soil erosion rates under the SSP 126 scenario are projected to be higher than that under the SSP 585 scenario. The ensemble average soil erosion rate is projected to increase by 15.41%–25.94% toward the end of the century for different emission scenarios, and the areas susceptible to high and very high soil erosion rates are projected to increase to 40.3%.

Climate change is considered one of the major threats to species extinction. The impact of climate change on the distribution of Aconitum heterophyllum, an endangered species in the northwestern Himalayan state of Himachal Pradesh, remain largely unexplored. In this study, species occurrence data, bioclimatic variables and population distribution data were used to map the current and future distribution (2050 and 2070) of A. heterophyllum. The Species Distribution Modelling (SDM) based on Maximum Entropy (MaxEnt) algorithm driven by climate data from the Global Circulation Model, HadGEM3-GC31-LL, which is statistically downscaled to 1 km spatial resolution was used for species distribution mapping. Here, we consider three future scenarios: Shared Socioeconomic Pathways (SSPs) - SSP126, SSP245, and SSP585. The Bioclimatic variables (Bio 15), which is precipitation seasonality and elevation, were found to positively influence the distribution of A. heterophyllum in the studied locations. Precipitation seasonality ensures adequate water availability at cold and dry habitats. Also, higher elevations corresponded to high suitable habitats in the Himalaya. The SDM predicted a total suitable area of 1863.7 km2 A. heterophyllum in Himachal Pradesh. Under SSP126, which represents moderate development with minimal environmental degradation, the suitable habitat is projected to decrease by 51.28%by 2070. Under SSP245, which represents moderate development with more pronounced environmental degradation, the suitable habitat is predicted to decrease by 53.64%in the mean by 2070. Under SSP585, representing fossil-fuelled development and successful mitigation of environmental issues, the suitable habitat is predicted to decrease by 54.61% by 2070. Overall, the species is expected to loose 30.68–58.51% of its current habitat between 2050 to 2070, posing a significant extinction risk in the future. Based on the classified layers, the highly suitable areas were found to be overlaying within the Dhauladhar ranges, alpine regions of Pin Valley National Park, Killar ranges of Chamba, Great Himalayan National Park, Parvati glacier, Gramphu, Indrasan Peak and Inderkilla National Park. These regions were identified as areas for key conservation efforts and are crucial for implementing adaptive management strategies to enhance the protection and sustainable use of A. heterophyllum in Himachal Pradesh in the face of global climate change.Keywords: Species Distribution Modelling, Northwestern Himalaya, Shared Socioeconomic Pathways, Climate change, Endangered

The ongoing and projected future climate change impacts the heating, cooling, and air-conditioning sectors both directly and indirectly. The consideration of heating, cooling, and energy degree-days is a consistent, robust, and widely used approach for quantitatively estimating the energy demand of closed environments based on outdoor thermal conditions. Hence, the spatial distribution and the long-term changes in this demand depend on on the quantity of final users for such services; it is essential to consider demographic data in the assessment. The paper presents a comprehensive analysis of the population-weighted degree-days for the near past and the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) scenario-driven future over Southeast Europe for all four ‘Tier 1’ Shared Socioeconomic Pathways (SSPs) based on the methodology of the United Kingdom Meteorological Office and performed using large NEX-GDDP CMIP6 ensemble of global circulation models (GCMs) and up to date population dynamics data from the NASA’s SEDAC. As an expression of regional warming tendencies, the study reveals an overall reduction in heating and an increase in cooling degree-days, confirming the leading role of the climate. We also provide evidences for the influence of the population factor, which significantly alters the region’s degree-day climatology in both space and time. The resulting overall picture on country-wide and regional level is complex; in some cases, the population dynamics is projected to outbalance the thermal-induced changes.

Effect difference of climate change and urbanization on extreme precipitation over the Guangdong-Hong Kong-Macao Greater Bay Area

Influences of the atmospheric patterns on unstable climate-growth associations of western Mediterranean forests

Potential impacts of future climate on the spatio-temporal variability of landslide susceptibility in Iran using machine learning algorithms and CMIP6 climate-change scenarios

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Arctic heat and freshwater budgets are highly sensitive to volume transports through Arctic-Subarctic straits. Here we investigate how the volume transports through these straits adjust to each other to maintain a mass balance in the Arctic on annual timescales. To this end, we use three models; two coupled global climate models, one with a third-degree horizontal ocean resolution (HiGEM1.1) and one with a twelfth-degree horizontal ocean resolution (HadGEM3), and one ocean-only model with an idealized polar basin (tenth-degree horizontal resolution). The two global climate models indicate that there is a strong anti-correlation between the Bering Strait throughflow and the transport through the Nordic Seas, a second strong anti-correlation between the transport through the Canadian Artic Archipelago (CAA) and the Nordic Seas transport, and a third strong anti-correlation between the Fram Strait and the Barents Sea throughflows. We find that part of the strait correlations is due to the strait transports being coincidentally driven by large-scale atmospheric forcing patterns such as the Arctic Oscillation. However, there is also a role for fast wave adjustments of some straits flows to perturbations in other straits since atmospheric forcing of individual strait flows alone cannot lead to near mass balance fortuitously every year. Idealized experiments with an ocean model (NEMO3.6) that investigate such causal strait relations suggest that perturbations in the Bering Strait are compensated preferentially in the Fram Strait due to the narrowness of the western Arctic shelf and the deeper depth of the Fram Strait. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;

Tree rings are natural archives that annually record distinct types of past climate variability depending on the parameters measured. Here, we use ring-width and stable isotopes in cellulose of trees from the northwestern Iberian Peninsula (IP) to understand regional summer hydroclimate over the last 400 years and the associated atmospheric patterns. Correlations between tree rings and climate data demonstrate that isotope signatures in the targeted Iberian pine forests are very sensitive to water availability during the summer period, and are mainly controlled by stomatal conductance. Non-linear methods based on extreme events analysis allow for capturing distinct seasonal climatic variability recorded by tree-ring parameters and asymmetric signals of the associated atmospheric features. Moreover, years with extreme high (low) values in the tree-ring records were characterised by coherent large-scale atmospheric circulation patterns with reduced (enhanced) moisture transport onto the northwestern IP. These analyses of extremes revealed that high/low proxy values do not necessarily correspond to mirror images in the atmospheric anomaly patterns, suggesting different drivers of these patterns and the corresponding signature recorded in the proxies. Regional hydroclimate features across the broader IP and western Europe during extreme wet/dry summers detected by the northwestern IP trees compare favourably to independent multicentury sea level pressure and drought reconstructions for Europe. Historical records also validate our findings that attribute non-linear moisture signals recorded by extreme tree-ring values to distinct large-scale atmospheric patterns and allow for 400-year reconstructions of the frequency of occurrence of extreme conditions in late spring and summer hydroclimate.