Climate Model Projections Research Articles

Fast upper-level jet stream winds get faster under climate change

Earth’s upper-level jet streams influence the speed and direction of travel of weather systems and commercial aircraft, and are linked to severe weather occurrence. Climate change is projected to accelerate the average upper-level jet stream winds. However, little is known about how fast (>99th percentile) upper-level jet stream winds will change. Here we show that fast upper-level jet stream winds get faster under climate change using daily data from climate model projections across a hierarchy of physical complexity. Fast winds also increase ~2.5 times more than the average wind response. We show that the multiplicative increase underlying the fast-get-faster response follows from the nonlinear Clausius–Clapeyron relation (moist-get-moister response). The signal is projected to emerge in both hemispheres by 2050 when considering scenario uncertainty. The results can be used to explain projected changes in commercial flight times, record-breaking winds, clear-air turbulence and a potential increase in severe weather occurrence under climate change.

A framework for coastal flood hazard assessment under sea level rise: Application to the Persian Gulf

Coastal areas are of paramount importance due to their pivotal role in facilitating a wide range of socio-economic activities and providing vital environmental services. These areas, as the meeting points of land and sea, face significant risks of flooding due to the ongoing rise in sea levels caused by climate change. Additionally, they are susceptible to extreme events like king tides and large waves in the future. This paper introduces a framework for estimating the extreme total water level (TWL) by considering the effects of regional sea level rise (RSLR) resulting from a warming climate under RCP 8.5. It also incorporates the contributions of high tides, 100-year storm surge, and 100-year wave setup and run-up. The proposed framework is utilized to evaluate the occurrence of extreme coastal flooding along the Persian Gulf coast of Iran, an area that is home to significant industries in the country. The results offer an estimated increase of RSLR by 0.23 m from 2020 to 2050 considering an ensemble of climate model projections. Extreme wave setup values are estimated to range between 0.19 and 0.66 m, while storm surge is projected to vary from 0.4 to 1.44 m across the studied coastline. These together yield in a projected extreme TWL along the coastline within the range of 3.18 and 3.90 m above the current sea level. This significant increase in sea level could lead to the inundation of approximately 513 km2 of low-lying coastal land, which accounts for about 16% of the studied domain and could pose serious flooding threat to the people and their assets in this region. Finally, relative ranking of flooded zones (i.e., 6 zones) helps determine the areas with higher chance of flood exposure, at which investment in flood mitigation measures should be prioritized.

Assessing SimCLIM climate model accuracy in projecting Southern Levantine basin air temperature trends up to 2100

This study evaluates the validity of forecasting air temperature ranges in 2100 using the SimCLIM climate projection model at spatial and temporal scales within the Southern Levantine basin. The model utilized historical air temperature data from 2000 to 2016, collected at seven southeastern Mediterranean stations, as well as 74 climate pattern ensembles integrated within SimCLIM. A combination of 40 global climate models (GCMs) and IPCC AR5 greenhouse gas emissions scenarios embedded in SimCLIM was employed to forecast mean, minimum, and maximum temperatures for 2100.The findings reveal that the average temperature increase in 2100, relative to the representative concentration pathways 2.6, 4.5, 6.0, and 8.5, will range between 0.8–1.17 °C, 1.48–2.0 °C, 2.1–3.8 °C, and 3.9–4.6 °C, respectively. Due to its acceptable accuracy, the SimCLIM model, incorporating 40 GCMs and 74 climate pattern ensembles, is highly recommended for forecasting future climate conditions. The model was evaluated using available temperature records in the study area, yielding a prediction percentage error of 2%, which strongly supports the use of SimCLIM.

Partitioning model uncertainty in multi-model ensemble river flow projections

Floods are the largest natural disaster currently facing the UK, whilst the incidents of droughts have increased in recent years. Floods and droughts can have devastating consequences on society, resulting in significant financial damage to the economy. Climate models suggest that precipitation and temperature changes will exacerbate future hydrological extremes (i.e., floods and droughts). Such events are likely to become more frequent and intense in the future; thus to develop adaptation plans climate model projections feed hydrological models to provide future water resource projections. ‘eFLaG’ is one set of future river flow projections produced for the UK driven by UKCP18 climate projections from the UK Met Office. The UKCP18-derived eFLaG dataset provides state-of-the-art projections for a single GCM driven by RCP 8.5 across the entire UK. A QE-ANOVA approach has been used to partition contributing sources of uncertainty for two flow quantiles (Q5 high flows and Q95 low flows), at near and far future time scales, for each of the 186 GB catchments in the eFLaG dataset. Results suggest a larger hydrological model uncertainty associated with low flows and greater regional climate model uncertainty for high flows which remains stationary between flow indicators. Total uncertainty increases from near to far future and highly uncertain catchments have been identified with a high concentration in South-East England.

Meteorological and Hydrological Drought Risks under Future Climate and Land-Use-Change Scenarios in the Yellow River Basin

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts.

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems.

Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergize with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition, as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process-based models of chemical fate that do not typically allow for such high-throughput applications. Here, we describe a Bayesian network model that does. It incorporates a two-step univariate regression model based on a 30-day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in-stream exposures. When relating to aquatic risks, the influence of these processes is reduced and distributions of risk are dominated by effect-related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient >1) was 8%-12%. This percentage further increased to 30%-35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384-400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Does drought stress eliminate the benefit of elevated CO2 on soybean yield? Using an improved model to link crop and soil water relations

Crop simulation models are indispensable tools that facilitate studies to assess climate impacts and adaptation responses, but have not been adequately tested in terms of accurately predicting crop growth and water stress responses to high carbon dioxide concentrations [CO2]. The soybean model, GLYCIM, previously modified with a coupled leaf-level gas exchange – energy balance model, was integrated with a two-dimensional convective-diffusive root growth module which linked soil and leaf water potentials with the regulation of stomatal conductance. We evaluated the accuracy of this modified GLYCIM using experimental data from a Free-Air CO2 Enrichment (FACE) site, SoyFACE, and used the calibrated model to quantify current and projected elevated CO2 effects on soybean yields in the United States (US) Midwest. The model accurately matched SoyFACE observed data including canopy temperature (RMSE, 2.26 -2.42 °C; IA, 0.89-0.90), photosynthesis rate (RMSE, 6.33 -12.88 µmol m−2 s−1; IA, 0.82-0.92), seasonal LAI (RMSE, 0.96 -1.08 cm2 cm−2; IA, 0.91-0.93), biomass (RMSE, 2.06 -2.34 Mg ha−1; IA, 0.87-0.90), soil water contents (RMSE, 5.42 -7.82 %; IA, 0.80-0.85) and root length (RMSE, 2.12 -5.64 m plant−1; IA, 0.70-0.86). GLYCIM also reproduced the observed declining response of yield to CO2 fertilization with increasing drought. Using an ensemble of five climate model projections, GLYCIM showed that the CO2 fertilization effect on yield was highest in northern counties and declined in southern and the western counties by late 21st century in response to increased drought characterized by higher daily maximum temperature and decreased rainfall. This study highlights the need to continue to test and improve crop models and use geospatially referenced adaptive strategies to capitalize the benefits from elevated [CO2] to better counteract increasing drought.

Crop production and water quality under 1.5 °C and 2 °C warming: Plant responses and management options in the mid-Atlantic region

The 2015 “Paris Agreement” aims to limit the global average temperature rise to significantly less than 2 °C, preferably within 1.5 °C above pre-industrial levels. A multitude of studies have focused on evaluating how different sectors respond to such levels of warming. Nonetheless, most of these studies fail to provide a clear roadmap to mitigate these impacts. A case in point is the anticipated decline in corn and soybean yields and increased phosphorus (P) and nitrogen (N) discharge into water bodies, a trend linked to past agricultural practices and climate change. In this research, we employ a novel assessment of how existing management practices under 1.5 °C and 2 °C global warming (GW) scenarios can affect nutrient availability in time and space as well as crop yield in a typical agricultural watershed in the Mid-Atlantic Region, specifically the Upper Maurice River Watershed (UMRW) in New Jersey.Using the Soil and Water Assessment Tool (SWAT) with multiple Global Climate Model (GCM) projections, we found that compared to 1.5 °C, a 2 °C GW scenario would exacerbate runoff, leading to amplified nutrient leaching. These losses decrease nutrient availability during the crop growing season. Moreover, a mismatch between the timing of fertilizer application and crop nutrient absorption caused nutrient-related stress. This nutrient and anticipated temperature stress resulted in a more significant decrease in crop yields under the 2 °C GW scenario than the 1.5 °C scenario. We have designed a management scenario to reduce future nutrient losses while increasing crop yields. The strategy involves altering the timing of planting/harvesting and the fertilizer application rate in response to a warming climate. This approach is projected to increase corn and soybean yields by +39 % (+21 %) and +2 % (+17 %), respectively, under the 1.5 °C (2.0 °C) GW scenario for the RCP-4.5 pathway. Simultaneously, it is expected to decrease the N and P loads at 1.5 °C (2.0 °C) GW. Comparable projections are also observed under the RCP-8.5 pathway.

Changes in solar resource intermittency and reliability under Australia’s future warmer climate

The dependency of photovoltaic (PV) power generation on meteorological parameters can impact power production due to weather-induced variability. During the day, fluctuations in radiation introduce intermittency in power generated, raising reliability and grid stability issues at higher penetration levels. Long-term future resource assessment provides an effective tool for estimating resource reliability and future intermittency essential for pre-feasibility site assessments around the world. Australia has high solar power capacity, with several solar farms in operational and developmental stage. Using Australia as a case study, this research aims to understand Australia's solar resource distribution and variability using regional climate model projections under a high emission scenario. Results indicate an abundance of solar resource power density in Australia, especially in the North (450-500Wm−2). The solar resource will be more reliable in Eastern Australia in the future with ∼ 5% increase in resource density. Results suggest reduction in intermittency (∼20-minute lull periods) in the East with increase in clear-sky days/year in the future (∼20 days/year). Resource assessment of Sun Cable and New England solar farm located in Australia, revealed the future scope of increase in clear-sky days at the sites. This long-term future solar variability analysis can help identify regions in Australia where PV systems will be least susceptible to losses due to intermittency. Furthermore, this study will help in critical decision-making processes like planning storage systems, site selection, opportunities to create hybrid solar farms with the co-existence of solar and wind technology, etc., to mitigate the risks associated with future intermittent PV power generation.

Tropical Pacific Modulation of the Asian Summer Monsoon Over the Last Millennium in Paleoclimate Data Assimilation Reconstructions

AbstractLarge uncertainties exist in climate model projections of the Asian summer monsoon (ASM). The El Niño‐Southern Oscillation (ENSO) is an important modulator of the ASM, but the ENSO‐ASM teleconnection is not stationary. Furthermore, teleconnections between ENSO and the East Asian versus South Asian subcomponents of the ASM exhibit distinct characteristics. Therefore, understanding the variability of the ENSO‐ASM teleconnection is critical for anticipating future variations in ASM intensity. To this end, we here use paleoclimate records to extend temporal coverage beyond the instrumental era by millennia. Recently, data assimilation techniques have been applied for the last millennium, which facilitates physically consistent, globally gridded climate reconstructions informed by paleoclimate observations. We use these novel data assimilation products to investigate variations in the ENSO‐ASM relationship over the last 1,000 years. We find that correlations between ENSO and ASM indices are mostly negative in the last millennium, suggesting that strong ASM years are often associated with La Niña events. During periods of weak correlations between ENSO and the East Asian summer monsoon, we observe an El Niño‐like sea surface temperature (SST) pattern in the Pacific. Additionally, SST patterns associated with periods of weak correlations between ENSO and South Asian summer monsoon rainfall are not consistent among data assimilation products. This underscores the importance of developing more precipitation‐sensitive paleoclimate proxies in the Indian subcontinental realm over the last millennium. Our study serves as a baseline for future appraisals of paleoclimate assimilation products and an example of informing our understanding of decadal‐scale ENSO‐ASM teleconnection variability using paleoclimate data sets.

Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India

AbstractGlobal climate model (GCM) projections are subject to significant uncertainties. Quantifying uncertainties in climate change projections improves credibility and makes climate data more reliable. This study aims to quantify the uncertainties in projected extreme precipitation during the 21st century over the homogeneous rainfall regions of India simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs. The percentile‐based square root error variance (SREV) method estimates model, scenario and ensemble uncertainties in projections of extreme precipitation. The uncertainty is investigated at four thresholds: 95th, 99th, 99.9th and 100th percentiles. The results show that the wet northeast region has a greater SREV, which is consistent with previous studies. At 99th and 99.9th percentiles, relative model SREV is dominant over the northeast (NE) region. However, at the 95th percentile high relative model SREV is found over the northwest (NW) region during southwest (June, July, August and September) and NE (October, November and December) monsoon seasons. Model uncertainty is the main source of uncertainty, followed by scenario and ensemble uncertainties. The study indicates that the arid NW region in India has a higher level of uncertainty than other regions with homogeneous rainfall. These findings will assist policymakers in planning infrastructure development in arid regions of India.

The Southern Ocean carbon and climate observations and modeling (SOCCOM) project: A review

The Southern Ocean serves as the primary gateway through which the intermediate, deep, and bottom waters of the ocean interact with the surface ocean (and thus the atmosphere), and it has a profound influence on the oceanic uptake of anthropogenic carbon and heat as well as nutrient resupply from the abyss to the surface. Yet it has been the least observed and understood region of the world ocean. The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project was implemented in 2014 with a goal to help remedy this deficit in observations and understanding. The SOCCOM project is based on two major advances that have the potential to transform understanding of the Southern Ocean. The first is the development of new biogeochemical sensors mounted on autonomous profiling floats that allow sampling of ocean biogeochemistry in 3-dimensional space. Floats may detect processes with a temporal resolution that ranges from hours to years. The second is that the climate modeling community finally has the computational resources and physical understanding to develop fully coupled climate models that can represent crucial, mesoscale processes in the Southern Ocean, as well as corresponding models that assimilate observations to produce a state estimate. The observational component, based on deployment of profiling floats with oxygen, nitrate, pH and bio-optical sensors, is generating vast amounts of new biogeochemical data that provide a year-round view of the Southern Ocean from the surface to 2000 m. The modeling effort is applying these observations and enhancing our understanding of the current ocean, and reducing uncertainty in projections of future carbon and nutrient cycles and climate. After nine years of operation, including a project renewal in the sixth year, the SOCCOM project has deployed more than 260 profiling floats. These floats have collected over 27,000 vertical profiles throughout the Southern Ocean. A data-assimilating biogeochemical state estimate model has been implemented. Here, the design of the SOCCOM project is reviewed and the scientific results that have been obtained are described. The project’s capability to help meet the observing system priorities outlined for a notional UN Decade for Ocean Sciences Southern Ocean observing system is assessed.

Climate change information tailored to the agricultural sector in Central Europe, exemplified on the region of Lower Franconia

There is a growing societal, economic, and political demand to translate available data on regional climate change into sector-specific, practice-oriented, and user-friendly information. The study presents a demand-driven approach to specify the impacts of regional climate change on agriculture, viticulture, and fruit and vegetable growing in Lower Franconia, southern Germany, a region with heterogeneous topography, diversified land use patterns, and intense activities in the sectors specified above. The approach is based on an ensemble of high-resolution regional climate model projections, a bias correction tool, and a large spectrum of meteorological (extreme) indicators that are crucial to the agricultural sector in Central Europe, as inferred from a stakeholder survey.For several decades, Lower Franconia represents a hotspot region of climate change with enhanced heat waves, prolonged droughts, and intermittent local flooding by heavy rainfall events. Results of the high-resolution regional climate model projections indicate an increase of hot days and tropical nights by a factor of 5 and 12, respectively, if greenhouse gas emissions continue to grow until 2100 according to the RCP8.5 emission scenario. At the same time, droughts will occur more frequently and last longer while rainfall intensity enhances. A longer growing period starting more than 40 days earlier (compared to the reference period 1970 to 1999) implies a higher risk of late frost damage for crops, fruits, grapes, and even some tree species. In contrast, the thermal prerequisites for viticulture will be satisfied across the entire region, even at higher-elevation sites. These facets of regional climate change are made accessible to users and the public via an interactive field-resolving web portal. Altogether, they gravely challenge the historically developed land use systems in Lower Franconia and require timely adaptation and mitigation strategies.

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of “Urban Development - Ecological Conservation” and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

Filter-feeding gelatinous macrozooplankton response to climate change and implications for benthic food supply and global carbon cycle.

It is often suggested that gelatinous zooplankton may benefit from anthropogenic pressures of all kinds and in particular from climate change. Large pelagic tunicates, for example, are likely to be favored over other types of macrozooplankton due to their filter-feeding mode, which gives them access to small preys thought to be less affected by climate change than larger preys. In this study, we provide model-based estimate of potential community changes in macrozooplankton composition and estimate for the first time their effects on benthic food supply and on the ocean carbon cycle under two 21st-century climate-change scenarios. Forced with output from an Earth System Model climate projections, our ocean biogeochemical model simulates a large reduction in macrozooplankton biomass in response to anthropogenic climate change, but shows that gelatinous macrozooplankton are less affected than nongelatinous macrozooplankton, with global biomass declines estimated at -2.8% and -3.5%, respectively, for every 1°C of warming. The inclusion of gelatinous macrozooplankon in our ocean biogeochemical model has a limited effect on anthropogenic carbon uptake in the 21st century, but impacts the projected decline in particulate organic matter fluxes in the deep ocean. In subtropical oligotrophic gyres, where gelatinous zooplankton dominate macrozooplankton, the decline in the amount of organic matter reaching the seafloor is reduced by a factor of 2 when gelatinous macrozooplankton are considered (-17.5% vs. -29.7% when gelatinous macrozooplankton are not considered, all for 2100 under RCP8.5). The shift to gelatinous macrozooplankton in the future ocean therefore buffers the decline in deep carbon fluxes and should be taken into account when assessing potential changes in deep carbon storage and the risks that deep ecosystems may face when confronted with a decline in their food source.

Predicting sub‐continental fuel hazard under future climate and rising atmospheric CO2 concentration

Abstract Bushfire fuel hazard is determined by the type, amount, density and three‐dimensional distribution of plant biomass and litter. The fuel hazard represents a biological control on fire danger and may change in the future with plant growth patterns. Rising atmospheric CO2 concentration (Ca) stimulates plant productivity (‘fertilisation effect’) but also alters climate, leading to a ‘climatic effect’. Both effects have impacts on future vegetation and thus fuel hazard. Quantifying these effects is an important component of predicting future fire regimes and evaluating fire management options. Here, we combined a machine learning algorithm that incorporates the power of large fine spatial resolution (i.e. 90 m) datasets with a novel optimality model that accounts for the climatic and fertilisation effects on vegetation cover. We demonstrated the usefulness and practicality of this framework by predicting fuel hazard across the state of Victoria in Australia. We fitted and evaluated the models with long‐term (i.e. 20 years), ground‐based fuel observations. The models achieved strong agreement with observations across the fuel hazard range (accuracy >65%). We found fuel hazard increased more in dry environments due to future climate and Ca. The contribution of the ‘fertilisation effect’ to future fuel hazard varied spatially by up to 12%. The predictions of future fuel hazard are directly useful to inform fire mitigation policies and as a reference for climate model projections to account for fire impacts. Synthesis and applications: Climate change and rising Ca have profound impacts on vegetation and thus fuel load. Operational fire management and future fire risk forecasts will benefit from our realistic fuel load prediction framework that incorporates plant responses and fine soil and terrain attributes.

Observational and model evidence together support wide-spread exposure to noncompensable heat under continued global warming.

As our planet warms, a critical research question is when and where temperatures will exceed the limits of what the human body can tolerate. Past modeling efforts have investigated the 35°C wet-bulb threshold, proposed as a theoretical upper limit to survivability taking into account physiological and behavioral adaptation. Here, we conduct an extreme value theory analysis of weather station observations and climate model projections to investigate the emergence of an empirically supported heat compensability limit. We show that the hottest parts of the world already experience these heat extremes on a limited basis and that under moderate continued warming parts of every continent, except Antarctica, will see a rapid increase in their extent and frequency. To conclude, we discuss the consequences of the emergence of this noncompensable heat and the need for incorporating different critical thermal limits into heat adaptation planning.

Climate models underestimate the sensitivity of Arctic sea ice to carbon emissions

Arctic sea ice has steadily diminished as atmospheric greenhouse gas concentrations have increased. Using observed data from 1979 to 2019, we estimate a close contemporaneous linear relationship between Arctic sea ice area and cumulative carbon dioxide emissions. For comparison, we provide analogous regression estimates using simulated data from global climate models (drawn from the CMIP5 and CMIP6 model comparison exercises). The carbon sensitivity of Arctic sea ice area is considerably stronger in the observed data than in the climate models. Thus, for a given future emissions path, an ice-free Arctic is likely to occur much earlier than the climate models project. Furthermore, little progress has been made in recent global climate modeling (from CMIP5 to CMIP6) to more accurately match the observed carbon-climate response of Arctic sea ice.

The impact of current and future climates on spatiotemporal dynamics of influenza in a tropical setting.

Although the drivers of influenza have been well studied in high-income settings in temperate regions, many open questions remain about the burden, seasonality, and drivers of influenza dynamics in the tropics. In temperate climates, the inverse relationship between specific humidity and transmission can explain much of the observed temporal and spatial patterns of influenza outbreaks. Yet, this relationship fails to explain seasonality, or lack there-of, in tropical and subtropical countries. Here, we analyzed eight years of influenza surveillance data from 12 locations in Bangladesh to quantify the role of climate in driving disease dynamics in a tropical setting with a distinct rainy season. We find strong evidence for a nonlinear bimodal relationship between specific humidity and influenza transmission in Bangladesh, with highest transmission occurring for relatively low and high specific humidity values. We simulated influenza burden under current and future climate in Bangladesh using a mathematical model with a bimodal relationship between humidity and transmission, and decreased transmission at very high temperatures, while accounting for changes in population immunity. The climate-driven mechanistic model can accurately capture both the temporal and spatial variation in influenza activity observed across Bangladesh, highlighting the usefulness of mechanistic models for low-income countries with inadequate surveillance. By using climate model projections, we also highlight the potential impact of climate change on influenza dynamics in the tropics and the public health consequences.

Anthropogenic fingerprints in daily precipitation revealed by deep learning

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1–4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.