Future Climate Variability Research Articles

Climate variability, population growth, and globalization impacting food security in Pakistan.

Global hunger has increased recently, returning to levels observed a decade ago. Climate change is identified as a key factor contributing to these increases and is a significant cause of severe food crises. When combined with population growth and globalization, future climate variability is expected to significantly affect global food security. This study examined the influence of climate variability, globalization, and population growth on food security (FS) in Pakistan from 1995 to 2023. Food security data was sourced from the Food and Agricultural Organization, and globalization data was obtained from the KOF Globalization Index. Datasets on climate variability, population growth, unemployment, and inflation were collected from the World Development Indicators. A two-layer principal component analysis was utilized to develop the food security index. Multivariate Generalized Auto-Regressive Conditional Heteroscedasticity was used to assess the impact of globalization, population growth, and climate variability on food security. All indexed components explained more than 50% of the variance, resulting in a food security eigenvalue of 3.603 with a 0.901 proportion. The findings indicated that population growth and climate variability significantly negatively affect Pakistan's food security. Furthermore, the study found that globalization notably mitigates the adverse effects of climate variability and population growth on food security. This research may help alleviate the risk of future undernourishment, reverse current trends of escalating food insecurity, and support the sustainable development goal of eliminating global hunger by 2030.

The shifting of buffer crop repertoires in pre-industrial north-eastern Europe

This study explores how major climatic shifts, together with socioeconomic factors over the past two millennia, influenced buffer crop selection, focusing on five crops: rye, millet, buckwheat, oat, and hemp. For this study, we analyzed archaeobotanical data from 135 archaeological contexts and historical data from 242 manor inventories across the northeastern Baltic region, spanning the period from 100 to 1800 AD. Our findings revealed that rye remained a main staple crop throughout the studied periods reflecting environmental adaptation to northern latitudes. The drought-tolerant and thermophilic millet crop exhibited resilience during the adverse dry climatic conditions of the Medieval Climatic Anomaly while showing a significant decline during the Little Ice Age. During the period of post-1500 AD, a significant shift towards cold-resilient summer crops such as buckwheat and hemp is recorded. This study enhances our understanding of how historical agricultural systems responded to both socioeconomic factors and climatic change in northern latitudes, offering notable potential solutions for modern agricultural practices in the face of future climate variability trends.

Enhanced interannual variability of the terrestrial carbon sink in China under high emissions

Abstract Interannual variability (IAV) of terrestrial carbon uptake is a major contributor to the variation of atmospheric CO2. With the influence of the East Asian monsoon, future climate variability would significantly increase in China. However, how these future changes will modulate the IAV of China’s terrestrial carbon sinks remains unclear. Here, we analyzed the IAV of China’s terrestrial net ecosystem productivity (NEPIAV) and investigated the potential impacts of climate change under various scenarios during the 21st century using the outputs from nine Coupled Model Intercomparison Project Phase 6 models. The results reveal that China’s terrestrial NEPIAV would be enhanced under higher emissions scenarios from 2015 to 2100. The standard deviation of national NEPIAV under the SSP585 scenario rises by 12% compared with SSP126. The most prominent contribution to this enhancement in the total NEPIAV comes from a larger NEP IAV in summer (10%), particularly in the subtropical–tropical monsoonal climate zone in China. Moreover, the enhancement is largely attributed to the intensified IAV in temperature and precipitation in the monsoonal climate zones as well as the heightened sensitivity to them, especially in the ecosystems of the subtropical–tropical monsoonal zone. Compared with monsoonal precipitation, IAV of temperature in the subtropical–tropical monsoonal climate zone also plays an important role in NEPIAV under higher emissions scenarios. Our results highlight the crucial influence of future fluctuations in monsoon climate systems on terrestrial carbon sink IAV and the urgency of reducing the uncertainties of Earth system models in predicting both climate in monsoon regions and the responses of carbon cycling processes to temperature.

Analysis of future climate variability under CMIP6 scenarios based on a downscaling method considering wet days in the upper Yangtze River basin, China

Abstract According to recent studies, the past decade was the hottest on record, and climate change is accelerating. As part of the Yangtze River Basin, the largest river basin in China, the Upper Yangtze River Basin (UYRB) plays a crucial role as the primary source of hydropower. However, the UYRB is also one of the most climate-sensitive regions within the basin, making the impact of climate change on this area particularly critical. We downscaled CMIP6 GCMs’ outputs of precipitation (including wet/dry spells sequence correction), and temperature projections (2024–2100), under four typical Shared Socioeconomic Pathways (SSPs), and we pursued a trend analysis upon these potential future climate series. We found significant upward trends in temperature across all SSPs in August, but no significant trends in precipitation for the same month. Additionally, for SSP370 and SSP585, there are significant upward trends in temperature in December, while precipitation showed no significant trends during that month. This may result in drier winters than now, increased potential evapotranspiration, and reduced surface (snow) water storage, impacting water resources availability. Consecutive dry/wet days at station, and basin scale show spatial-temporal heterogeneity, but generally wet spells are longer, and dry spells shorten moving from South-East to North-West.

Modelling climate change and aridity for climate impact studies in semi-arid regions: The case of Giba basin, northern Ethiopia.

of long-term and future climate variability is crucial for impact assessment studies in drought-prone areas like the Giba basin in northern Ethiopia. This study has applied the statistical downscaling model (SDSM) and (De Martonne and Pinna combinative) aridity index methods to evaluate the climate system of the Giba basin. Historical data (1961-2019) from seven meteorological stations and global grided data were used for future climate projections (2020-2100) under the three emission scenarios (RCPs 2.6, 4.5, and 8.5) for the three-time horizons (2040s, 2060s, and 2080s). Analysis of results showed that rainfall and temperature projection on a monthly and/or seasonal basis has more significance than on an annual basis for impact studies particularly, in areas where irrigation practices are common like in the Giba basin. Seasonal projection of rainfall in the basin showed a slightly decreasing trend during the spring season (MAM), and a significant increment in the main rainy season (JJA) under all scenarios and for the whole projection year. On an annual basis, a maximum increase of rainfall, up to +285mm/year and +298mm/year was expected to increase at Abyi Adi and Mekelle Obs stations, respectively, under RCP 8.5 in the 2080s. Temperature projection showed a consistent rise throughout the basin that ranges from a minimum increase of Tmax by +0.29°C in the 2040s (RCP 2.6) at Mekelle Obs station to a maximum increase of Tmin by +2.35°C in the 2080s (RCP8.5) at Abyi Adi station. In general, it is observed that the rate of increment of projected Tmin was more than that of Tmax in all stations in the Giba basin, which showed a continuous contraction of the gap between Tmin and Tmax, hence, the prevalence of global warming. This has led to a considerable increment of aridity till the end of the 21st century. Hence, the implementation of locally-suited climate change resilient strategies is crucial to enhance the sustainability of the ecosystem and ensure food security in the basin.

Prediction of future climate variability and its implications for rainfed agriculture in the Guder watershed, Ethiopia

Prediction of future climate variability and its implications for rainfed agriculture in the Guder watershed, Ethiopia

Regional Ecological Management Strategy of Nature’s Contribution to People (NCPs) on the Qinghai–Tibet Plateau

Balancing human needs with nature’s contributions is essential for sustainable development. The nature’s contribution to people (NCPs) framework helps identify ecosystem benefits; however, few studies have applied it to conservation priorities on the Qinghai–Tibet Plateau. Here, we first proposed an assessment method on the supply and demand of the critical NCPs, including habitat maintenance (NCP1), climate regulation (NCP4), and water quantity regulation (NCP6), and assessed NCPs from 2000 to 2020. Then, we evaluated the importance and vulnerability of NCPs based on the NCP scores and multiple stressors, which refer to current and future socioeconomic and climatic factors and inter-annual variability. Finally, we identified the ecological priority conservation areas of NCPs by comprehensively considering the importance and vulnerability. The results revealed that the following: (1) the supply and demand of three NCPs across the QTP showed similar spatial distributions, with higher values in the southeast and lower in the northwest, whereas NCP4 and NCP6 showed increasing trends over time; (2) the importance of NCPs was higher in the eastern and western parts of the QTP, while the vulnerability of NCPs was greater in the central and northwest areas, with NCP1 and NCP4 recording much higher vulnerability scores (0.94 and 0.90, respectively) compared to NCP6 (0.14); and (3) based on the importance and vulnerability of NCPs, the priority conservation areas were primarily in the eastern QTP (13.68%), covering the Yangtze and Lancang basins, with key maintenance areas focused in the southwest, underscoring a significant ecological conservation emphasis in the eastern region from both supply and demand perspectives. These insights into the NCPs’ ecological conservation support regional coordination and ecological management to enhance human well-being.

The effect of climate change on spatio-temporal activity in burrowing frogs of the Smilisca group

Measuring the potential effects of future climate changes on the spatio-temporal variance of optimal conditions for seasonal species is a key conservation issue. This study assesses the impact of climate change on the spatial and temporal patterns of optimal conditions for activity in two burrowing frogs, Smilisca fodiens and S. dentata. Ecological Niche Modeling was used to implement niche seasonality models, with calibration performed during the peak activity (July). These models were then transferred to current and future conditions for the remainder of the year, predicting future scenarios up to 2070 with an intermediate trajectory greenhouse gas concentration of 4.5 W/m2. Climate change transferability was assessed for four potential scenarios: 1) high precipitation and low temperature, 2) high precipitation and high temperature, 3) low precipitation and low temperature, and 4) low precipitation and high temperature. We examined the impact across future projected areas and analyzed geographic change trends based on latitude, longitude, and elevation. For both species, the best scenario would involve increased precipitation in the future. However, the worst-case would be a combination of reduced precipitation and higher temperatures. Due to large area loss, northern populations of S. fodiens may be highly vulnerable. Concerning S. dentata, the outlook is worrisome, with all known populations experiencing losses in most months. Area gains may not help either species since they tend to occur at elevations above their known ranges. Using a seasonal approach in spatio-temporal analysis enhances comprehension of the behavioral adaptations of seasonal species and their vulnerability to current and future climatic variations.

A 903-year annual temperature reconstruction for the southeastern Tibetan Plateau from the tree ring widths of Juniperus saltuaria

Precisely dated paleoclimatic records are essential for understanding natural and anthropogenic climate influences. Here, an annually resolved absolutely dated Juniperus saltuaria tree ring width chronology from the Haizi mountain, southeastern Tibetan Plateau (TP) was developed. The chronology shows the annual- to decadal-scale paleoclimatic variability of the southeastern TP over the past 903 years (1115–2017 CE). The tree ring widths correlate significantly with mean annual temperature (Tmean). A linear regression model between ring width and Tmean, accounting for 57% of the variance in temperature from 1959 to 2017 CE, was used to reconstruct the past 903 years of Tmean variation in the southeastern TP. The chronology aligns with other temperature records from the TP, Asia, and the Northern Hemisphere (NH), indicating a marked temperature increase since the late twentieth century, with 1998–2017 CE identified as the warmest period. The coldest thirty years occurred in 1115–1145 CE. Solar activity and the Atlantic Multidecadal Oscillation exert notable influences on temperature fluctuations in this region. Superposed epoch analysis indicates that volcanic eruptions had significantly impacted southeastern TP temperatures, causing dramatic cooling for 2–4 years. Our study presents the longest width chronology developed by Juniperus saltuaria to date, offering a long-term perspective on recent climatic shifts across the southeastern TP. This work enhances understanding of historical climate variability, providing critical insights to refine projections of future climate variability.

Modeling the distribution of cultural ecosystem services based on future climate variables under different scenarios

With the far-reaching consequences of worldwide climate variationon ecosystems and human societies, understanding and predicting changes in cultural ecosystem services (CES) is essential for sustainable development policy and resource planning. Past studies have focused on changes and impacts on natural ecosystems, while relatively few studies have been conducted on predictions of CES. This study combines POI datasets with future climate variables under different scenarios into the Maxent model for forecasting the spatial and temporal distribution of CES, which provides strong support for future decision-making. The results indicate that: (1) Under the SSP126 and SSP585 scenarios, the CES values in the northern, western, central, and northeastern parts of the study area are relatively high, while those in the southwestern, southern, and southeastern parts are relatively low. (2) Under the SSP126 scenario, the total CES shows an increasing trend from 2021 to 2040, but slightly declines from 2061 to 2080. In contrast, under the SSP585 scenario, the total CES significantly decreases from 2021 to 2040, especially in the provinces of Guizhou, Hunan, Zhejiang, and Anhui. (3) Temperature has a significant impact on CES predictions, with the annual mean temperature (Bio1) positively correlated with total CES, contributing between 0.75 and 0.78 to the distribution of CES across different years and scenarios. Additionally, the maximum temperature of the hottest month (Bio5) and the mean temperature of the wettest quarter (Bio8) also significantly influence CES under different scenarios and years. These findings reveal the regional characteristics and variations in CES distribution under different climate scenarios, providing crucial scientific evidence for future policy-making, resource management, and climate adaptation strategies. They also offer important insights into the impact of global climate change on ecosystems and human society, serving as a valuable reference for future national decision-making.

Advances in giant clam (Tridacnidae spp.) sclerochronology and sclerochemistry

Understanding past environmental change through high-resolution palaeoenvironmental proxy reconstructions provides crucial baselines for understanding global environmental changes, particularly during the Quaternary Period. Such data are crucial for testing and refining climate models to enable better adaptations to current and future climate changes. Giant clams (Tridacnidae spp.), the largest bivalve mollusc in the Indo-Pacific region, possess distinctive features such as long lifespan, rapid growth, high-resolution growth patterns, wide distribution habitat, and stable shell geochemical composition. These attributes make giant clams a promising and dependable novel palaeoenvironmental archive, offering numerous available geochemical proxies. In this review, we examined preview studies on giant clams conducted for sclerochronology research, spanning publications from 1986 to 2023. The previous studies have proven that isotopic and trace elements proxies of the giant clam, including δ18O, δ13C, Sr/Ca, Mg/Ca, Ba/Ca, and Fe/Ca, can faithfully reconstruct palaeoclimate records including sea surface temperature, dissolved inorganic carbon, insolation, extreme weather events and primary productivity at ultra-high resolution. These proxies have been successfully employed for palaeoclimatic and palaeoenvironmental reconstructions from the Miocene to the present across a range of locations from South Japan to Northern Australia to the Red Sea. These studies have illuminated changes in interannual climate cycles, climate variation, and human migrations over thousands to millions of years. They have generated quantitative records that are valuable for testing and refining numerical climate models, understanding the relationship between past climate cycles and future climate variability, and providing insights into human-environment interactions over time. In the end, the review also addresses the challenges and provided recommendations for future research, highlighting persisting gaps in understanding the influence of microstructure on the shell, age dating of fossil samples, uncertainty in proxies, the need for tank experiments, and access to ultra-high resolution palaeoenvironmental records.

Assessment of catchment water resources allocation under climate change in Luwombwa sub-catchment, Zambia

The uncertainty in climate change and high water demand pose pressure on the natural water resources supply. Not only does this require better understanding but also a call for immediate interventions, mitigation and adaptive measures. This study evaluates catchment water resources in the Luwombwa sub-catchment in Zambia through statistical analysis in the downscaling of past, present and future climatic variables from the CMIP6 climatic model. These variables are then integrated into WEAP - a semi-distributed hydrological and water evaluation model - to perform water demand and allocation scenario modelling. Mult-site calibration and validation were conducted on five selected micro-catchments within Luwombwa sub-catchment. The model performance was assessed usng the R2, NSE and PBIAS as the objective functions. Satisfactory values of 92 % for R2, 82 % for NSE and 6.9 % for PBIAS were achieved. This allowed for scenario modelling on water demand and allocation among competing users. Three future scenarios (2022–2050) were developed from the historical to baseline (1988–2022) and included state of water resources availability under climate change, expansion of irrigation area and impact of dam construction in the sub-catchment. The study reveals a decrease of 20 % in sub-catchment's water availability resulting from 9.3 % (equivalent to 4oC) rise in maximum temperature and 4.5 % reduction in rainfall within the entire sub-catchment. This is especially under the persistence of SSP370 climate variability scenario projections downscaled from four GCM models by the year 2050. The study further revealed that the change point for anticipated future climate extremes is likely to occur between 2027 and 2030. The results are indicative of downward trends in streamflow under climate change and socioeconomic development leading to increase in water value and water scarcity. The insights from the study are critical to inform formulation of effective catchment water resources management strategies such as the development of management plans and adapation measures in the face of climate change and the needs for different stakeholders involvement.

Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques

ABSTRACT Cloud properties are pivotal in analyzing rainfall patterns in monsoon-dependent countries such as India. The impact of climate change becomes more important in regions susceptible to hydrometeorological events due to different monsoon regimes. To examine regional heterogeneity of cloud properties, this study investigates long-term trends and predictive capabilities for cloud properties in drought- and flood-prone regions of western India, utilizing satellite data and employing machine learning (ML) models to comprehend intricate data patterns and enhance predictive accuracy. The results show higher mean and variability in cloud parameters over the flood-prone area due to favorable rain conditions, reflecting higher cloud microphysical and optical properties. These parameters negatively correlate with some cloud macrophysical properties and aerosol property in the drought-prone area. A moderate correlation exists between certain cloud characteristics of one region and another. Employing ML for regression analysis and comparing them for cloud effective radius across regions shows promising results, with random forest demonstrating high coefficient of determination (0.86, 0.93) and low root mean squared error (0.76, 1.15) due to its robustness and high accuracy. This research enhances the understanding of regional heterogeneity in India and shows that ML can help in predicting future cloud dynamics and climate by suitable model.

Prediction of the potential distribution of a raspberry (Rubus idaeus) in China based on MaxEnt model

Rubus idaeus is a pivotal cultivated species of raspberry known for its attractive color, distinct flavor, and numerous health benefits. It can be used in pharmaceutical, cosmetics, agriculture and food industries not only as fresh but also as a processed product. Nowadays due to climatic changes, genetic diversity of cultivars has decreased dramatically. However, until now, the status of wild R. idaeus resources in China have not been exploited. In this study, we investigated the resources of wild R. idaeus in China to secure its future potential and sustainability. The MaxEnt model was used to predict R. idaeus suitable habitats and spatial distribution patterns for current and future climate scenarios, based on wild domestic geographic distribution data, current and future climate variables, and topographic variables. The results showed that, mean temperature of the coldest quarter (bio11), precipitation of the coldest quarter (bio19), precipitation of the warmest quarter (bio18), and temperature seasonality (bio4) were crucial factors affecting the distribution of R. idaeus. Presently, the suitable habitats were mainly distributed in the north of China including Xinjiang, Inner Mongolia, Gansu, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Liaoning, Jilin, Heilongjiang. According to our results, in 2050s, the total suitable habitat area of R. idaeus will increase under SSP1-2.6 and then will be decreased with climate change, while in the 2090s, the total suitable habitat area will continue to decrease. From the present to the 2090s, the centroid distribution of R. idaeus in China will shift towards the east and the species will always be present in Inner Mongolia. Our results provide wild resource information and theoretical reference for the protection and rational utilization of R. idaeus.

Decision Support Framework for Water Quality Management in Reservoirs Integrating Artificial Intelligence and Statistical Approaches

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding.

Projection of Future Climatic Variables based on CMIP5 and CMIP6 Models in the Gedarchay Catchment (West Azarbaijan)

Projection of Future Climatic Variables based on CMIP5 and CMIP6 Models in the Gedarchay Catchment (West Azarbaijan)

The driving of North American climate extremes by North Pacific stationary-transient wave interference

Wave interference between transient waves and climatological stationary waves is a useful framework for diagnosing the magnitude of stationary waves. Here, we find that the wave interference over the North Pacific Ocean is an important driver of North American wintertime cold and heavy precipitation extremes in the present climate, but that this relationship is projected to weaken under increasing greenhouse gas emissions. When daily circulation anomalies are in-phase with the climatological mean state, the anomalous transport of heat and moisture causes the enhanced occurrence of cold extremes across the continental U.S. and a significant decrease of heavy precipitation extremes in the western U.S. In a future emissions scenario, the climatological stationary wave over the eastern North Pacific weakens and shifts spatially, which alters and generally weakens the relationship between wave interference and North American climate extremes. Our results underscore that the prediction of changes in regional wave interference is pivotal for understanding the future regional climate variability.

Controls on Oxygen Variability and Depletion in the Patuxent River Estuary

Oxygen depletion in coastal waters is increasing globally due primarily to eutrophication and warming. Hypoxia responses to nutrient loading and climate change have been extensively studied in large systems like the Chesapeake Bay and the Baltic Sea, while fewer studies have investigated smaller, shallower hypoxic zones. Thus, an improved understanding of the interactions of eutrophication and warming on hypoxia expansion (or reduction) in the wide variety of different estuarine environments is needed. We examined interannual controls on oxygen depletion in the Patuxent River estuary, a eutrophic sub-estuary of Chesapeake Bay where seasonal hypoxia develops annually. We conducted a spatial and temporal analysis of dissolved oxygen (DO) trends, timing, and several metrics of depletion over a long-term record (1985–2021). We found an internally generated hypoxic zone that initiates in the middle estuary, spreading upstream and downstream as the summer progresses, and that hypoxic volume days (HVD) have been increasing (0.11 per year, p = 0.03) over the record despite reduced watershed nitrogen loads and stable phosphorus loads. River flow and temperature have been increasing and are major drivers of increased HVD, with river flow explaining 40% of the interannual variation in HVD (temperature has increased 0.03 and 0.06 °C per year in summer and fall, respectively). Apparent oxygen utilization (AOU) is increasing in bottom waters in the fall, consistent with increasing trends of both water temperature and stratification strength. HVD was negatively related (r2 = 0.34, slope = −0.59*HVD) to the biomass of benthic invertebrates in the middle region of the estuary, suggesting that benthic forage for higher trophic levels will be limited by sustained hypoxia. These results indicate that current and future climate variability plays an important role in regulating oxygen depletion in the Patuxent River estuary, which reinforces the need to factor climate change into strategies for the restoration and management of estuaries.

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

AbstractProjecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.