Recently, climate change makes itself felt at increasing levels due to rising temperatures, irregular precipitation patterns and changing weather events. Although the frequently used Mann-Kendall (MK) method has disadvantages such as needing serial independence, it helps to detect monotonic trends to investigate climate change effects on a given time series. Climate change may have different features on different levels such as the lows and highs of a given time series, leading to non-monotonic trends. Innovative trend analysis (ITA) as an innovative trend analysis method detects non-monotonic trends, which MK cannot. In this study, MK method is improved to detect non-monotonic trends (non-monotonic MK) and applied for Murat River basin, a branch of Euphrates River, precipitation series at Bingöl, Muş, and Ağrı meteorological stations. Although classical MK method cannot detect any trend on the river basin, non-monotonic MK (NMK) method detects two important decreasing (increasing) trends on the low (high) values of Bingöl and Muş (Bingöl) stations. Also, stationarity analysis is applied through the statistical significance level concept for the river basin precipitation series using the NMK method. Bingöl station has a non-stationary precipitation series with a value of 3.07 and 95% confidence level, while Muş station has a remarkable value of 1.58, Ağrı station conserves its stationarity characteristic on the precipitation series. It is hoped that the newly developed NMK method will help to understand the effects of climate change on hydro-meteorological historical records and predict future events for more efficient hydraulic structure designs.
We present a first study showing that organization of trade cumulus (Tc) clouds can significantly enhance Tc response to climate change. Among four recently identified states of Tc organization, the “Flower” state has the highest and the “Sugar” state the lowest cloud fraction and cloud radiative effect. Using large-eddy simulations, we show that the organized “Flower” Tc state is strongly suppressed at the end of the 21st century, unlike the less organized “Sugar” Tc state and Tc studied previously. The primary cause of the suppression is down-welling long-wave radiation from increased greenhouse gas concentrations, which weakens the mesoscale circulation that organizes clouds into the “Flower” Tc state. The cumulus-valve mechanism, which is thought to limit Tc response to climate change, does not prevent this response. Our work unravels an unrecognized role of cloud organization in the cloud response to climate change.
In the past few decades, sea level rise (SLR) has been used as one of the most reliable proxies for evincing climate change impacts and significantly contributed to elevated coastal high-water levels around the globe. High tide flooding (HTF) has become more frequent along the U.S. coasts, and it is expected to become more frequent in the following decades. Thus, having an improved estimate of SLR along the coast is crucial for flood hazard mitigation and adaptation planning. There is a lack of a comprehensive framework that provides SLR and HTF flooding statistics at a reasonable spatial resolution that complements current point-based (tide gauge) estimations. To fill this gap, we developed a machine learning algorithm to extract the spatially distributed SLR and HTF thresholds using inputs from observational data. The outcome of this physics-informed machine learning methodology is SLR and HTF estimates under projected SLR by the mid-21st century Background
Michael Joseph Lee*1, Kathleen E. McLean1, Michael Kuo1, Gregory R.A. Richardson2, Sarah B. Henderson11Environmental Health Services, British Columbia Centre for Disease Control, 655 West 12th Ave, Vancouver, BC Canada2Extreme Heat Program, Climate Change and Innovation Bureau, Health Canada, 269 Laurier Ave W, Ottawa, On Canada*Corresponding author:Michael Leemichael.email@example.com
There are large uncertainties in our future projections of climate change at the regional scale, with spatial variabilities not resolved adequately by coarse-grained Earth System Models (ESMs). In this study, we use pseudo global warming simulations driven by end of the century upper end RCP (Representative Concentration Pathway) 8.5 projections from 11 state-of-the-art ESMs to examine changes in summer heat stress extremes using physiologically relevant heat stress metrics (heat index and wet bulb globe temperature) over the Great Lakes Region (GLR). These simulations, generated from a cloud-resolving model, are at a fine spatiotemporal resolution to detect heterogeneities relevant for human heat exposure. These downscaled climate projections are combined with gridded future population estimates to isolate population versus warming contributions to population-adjusted heat stress in this region. Our results show that a significant portion of summer will be dominated by critical outdoor heat stress levels within GLR for this scenario. Additionally, regions with higher heat stress generally have disproportionately higher population densities. Humidity change generates positive feedback on future heat stress, generally amplifying heat stress (by 24.2% to 79.5%) compared to changing air temperature alone, with the degree of control of humidity depending on the heat stress metric used. The uncertainty of the results for future heat stress are quantified based on multiple ESMs and heat stress metrics used in this study. Overall, our study shows the importance of dynamically resolving heat stress at population-relevant scales to get more accurate estimates of future heat risk in the region.
When multiple atmospheric rivers (ARs) occur in rapid succession, the compound effect on the hydrologic system can lead to more flooding and damage than would be expected from the individual events. This temporally compounding risk is a source of growing concern for water managers in California. We present a novel moving average-based definition of AR “sequences” that identifies the time periods of elevated hydrologic hazard that occur during and after consecutive AR events. This marks the first quantitative evaluation of when temporal compounding is contributing to AR flood risk. We also assess projected changes in sequence frequency, intensity, and duration in California under both intermediate (SSP2-4.5) and very high (SSP5-8.5) emissions scenarios. Sequence frequency increases over time and is fairly uniform across the state, with the largest changes occurring by the end of the century (+0.72 sequences/year in SSP2-4.5, +1.13 sequences/year in SSP5-8.5). Sequence intensity and duration both see increases in the central tendencies and extreme values of their respective distributions relative to the historic baselines. In particular, “super-sequence” events longer than sixty days are projected to occur 2-3x more frequently and to emerge in places that have never seen them in the historical record. In a world where California precipitation is becoming more erratic and temporally concentrated, our definition of sequences will help identify when and where hydrologic impacts will be most extreme, which can in turn support better management of the state’s highly variable water resources and inform future flood mitigation strategies.
Whether the presence of permafrost systematically alters the rate of riverbank erosion is a fundamental geomorphic question with significant importance to infrastructure, water quality, and biogeochemistry of high latitude watersheds. For over four decades this question has remained unanswered due to a lack of data. Using remotely sensed imagery, we addressed this knowledge gap by quantifying riverbank erosion rates across the Arctic and subarctic. To compare these rates to non-permafrost rivers we assembled a global dataset of published riverbank erosion rates. We found that erosion rates in rivers influenced by permafrost are on average six times lower than non-permafrost systems; erosion rate differences increase up to 40 times for the largest rivers. To test alternative hypotheses for the observed erosion rate difference, we examined differences in total water yield and erosional efficiency between these rivers and non-permafrost rivers. Neither of these factors nor differences in river sediment loads provided compelling alternative explanations, leading us to conclude that permafrost limits riverbank erosion rates. This conclusion was supported by field investigations of rates and patterns of erosion along three rivers flowing through discontinuous permafrost in Alaska. Our results show that permafrost limits maximum bank erosion rates on rivers with stream powers greater than 900 W/m-1. On smaller rivers, however, hydrology rather thaw rate may be dominant control on bank erosion. Our findings suggest that Arctic warming and hydrological changes should increase bank erosion rates on large rivers but may reduce rates on rivers with drainage areas less than a few thousand km2.
The decline of the eastern East African (EA) March-April-May (MAM) rains poses a life-threatening “enigma,” an enigma linked to sequential droughts in the most food-insecure region of the world. The MAM 2022 drought was the driest on record, preceded by three poor rainy seasons, and followed by widespread starvation. Connecting these droughts is an interaction between La Niña and climate change, an interaction that provides exciting opportunities for long-lead prediction and proactive disaster risk management. Using observations, reanalyses, and climate change simulations, we show here, for the first time, that post-1997 OND La Niña events are robust precursors of: (1) strong MAM “Western V Gradients” in the Pacific, which help produce (2) large increases in moisture convergence and atmospheric heating near Indonesia, which appear associated with (3) regional shifts in moisture transports and vertical velocities, which (4) help explain more frequent dry EA rainy seasons. Understanding this causal chain will help make long-lead forecasts more actionable. Increased Warm Pool atmospheric heating and moisture convergence sets the stage for dangerous sequential droughts in EA. At 20-year time scales, we show that these Warm Pool heating increases are attributable to observed Western V warming, which is, in turn, largely attributable to climate change. As energy builds up in the oceans and atmosphere, we see stronger convergence patterns, which offer opportunities for prediction. Hence, linking EA drying to a stronger Walker Circulation can help explain the “enigma” while underscoring the predictable risks associated with recent La Niña events.
Much of the world’s water resource infrastructure is experiencing rapid shifts in climate and snowmelt. Changing snowmelt regimes are responsible for rain-on-snow river flooding, putting communities at risk. Our study uses a new Snow Regime Classification system as a proxy for tracking climate driven changes in hydrology across the contiguous US over 40 years (1981-2020). Snow regimes are calculated annually, with changes evaluated across decadal and 30-year time scales. Our Snow Regime technique designates areas across CONUS as: (1) rain dominated (RD), (2) snow dominated (SD), (3) transitional (transient mix of rain and snow; R/S), or (4) as perennial snow cover (PS). Class thresholding ratios involve snow water equivalent (SWE) over cumulative cool-season precipitation (October through March).
Current global actions to reduce greenhouse gas emissions are very likely to be insufficient to meet the climate targets outlined under the Paris Agreement. This motivates research on possible methods for intervening in the Earth system to minimize climate risk while decarbonization efforts continue. One such hypothetical climate intervention is stratospheric aerosol injection (SAI), where reflective particles would be released into the stratosphere to cool the planet by reducing solar insolation. The climate response to SAI is not well understood, particularly on short-term time horizons frequently used by decision makers and planning practitioners to assess climate information. This knowledge gap limits informed discussion of SAI outside the scientific community. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no-SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and applies to the question “What happens before and after SAI is deployed in the model?” The second framing, the intervention impact, displays the difference between the SAI and no-SAI simulations, corresponding to the question “What is the impact of a given intervention relative to climate change with no intervention?” We apply these framings to annual mean 2-meter temperature, precipitation, and a precipitation extreme in the first two experiments to use large ensembles of Earth system models that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables.
This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, in 2022 23 million east Africans faced starvation, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamic models to predict droughts at eight-month lead-times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization (WMO) “Early Warning for All” plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance. The main messages of this commentary will be widely. Climate change is interacting with La Niña to produce extreme, but extremely predictable, Pacific sea surface temperature gradients. These gradients will affect the climate in many countries creating opportunities for prediction. Effective use of such predictions, however, will demand cross-silo collaboration.
In the ocean, temperature extremes have adverse effects on precipitation patterns, sea level change, and migration/damage of ecosystems. It has been found that most species are more sensitive to extreme events like marine heatwaves (MHWs), implying the severe impacts of MHWs on ecology. These events are driven by various atmospheric and oceanic processes. In recent years, these extreme events are more frequent and intense globally and their increasing trend is expected to continue in the upcoming decades. They have the potential to devastate marine habitats, and ecosystems together with ensuing socioeconomic consequences. It recently attracted public interest and scientific researchers, which motivates us to analyze the recent MHW events in the Bay of Bengal region. we have isolated 107 MHW events (above the 90th percentile threshold) in this region of the Indian Ocean and investigated the variation in duration, intensity, and frequency of MHW events during our test period (1982-2021). Our study reveals that the average of three MHW events per year in the study region with an increasing linear trend of 1.11 MHW events per decade. In the analysis, we found the most intense event has a maximum intensity was 5.29°C (above the climatology mean), while the mean intensity was 2.03°C. In addition, we observed net heat flux accompanied by anticyclonic eddies to be the primary cause of these events. Also, an effort has been made to understand the relationship between climate modes, sea surface height, and the difference between evaporation and precipitation with the occurrence of MHW events.
The rapid loss of Arctic Sea Ice (ASI) in the last decades is one of the most evident manifestations of anthropogenic climate change. A transition to an ice-free Arctic during summer would impact climate and ecosystems, both regionally and globally. The identification of Early-Warning Signals (EWSs) for the loss of the summer ASI could provide important insights into the state of the Arctic region. We collect and analyze CMIP6 model runs that reach ASI-free conditions (area below 10^6 km^2) in September. Despite the high inter-model spread, with the range for the date of an ice-free summer spanning around 100 years, the evolution of the summer ASI area right before reaching ice-free conditions is strikingly similar across the CMIP6 models. When looking for EWSs for summer ASI loss, we observe a significant increase in the variance of the ASI area before reaching ice-free conditions. This behavior is detected in the majority of the models, and also averaged over the ensemble. We find no increase in the 1-year-lag autocorrelation in model data, possibly due to the multiscale characteristics of climate variability, which can mask changes in serial correlations. However, in the satellite-inferred observations, increases in both variance and 1-year-lag autocorrelation have recently been revealed.
Hazards from convective weather pose a serious threat to the continental United States (CONUS) every year. Previous studies have examined how future projected changes in climate might impact the frequency and intensity of severe weather using simulations with both convection-permitting regional models and coarser climate and Earth system models. However, many of these studies have been limited to single representations of the future climate state with little insight into the uncertainty of how the population of convective storms may evolve. To thoroughly explore this aspect, a large ensemble of Earth system model simulations was implemented to investigate how forced responses in large-scale convective environments might be modulated by internal climate variability. Daily data from an ensemble of 50 simulations with the most recent version of the Community Earth System Model was used to examine changes in the severe weather environment over the eastern CONUS during boreal spring from 1870-2100. Results indicate that forced changes in convective environments were small between 1870 and 1990, but throughout the 21st century, convective available potential energy and atmospheric stability (convective inhibition) is projected to increase while 0-6 km vertical wind shear decreases. Internal climate variability can either significantly enhance or suppress these forced changes. The time evolution of bivariate distributions of convective indices illustrates that future springtime convective environments over the eastern CONUS will be characterized by relatively less frequent, less organized, but deeper, more intense convection. Future convective environments will also be less supportive of the most severe convective modes and associated hazards.
This is the first forecast of marine circulation and biogeochemistry for the Ascension Island Marine Protected Area (MPA). MPAs are a key management tools used to safeguard ocean biodiversity from human impacts, but their efficacy is increasingly threatened by anthropogenic climate change. To assess the vulnerability of individual MPAs to climate change and predict biological responses, it is first necessary to forecast how local marine environments will change. We found that the MPA will become warmer, more saline, more acidic, with less nutrients, less chlorophyll and less primary production by the mid-century. A weakening of the Atlantic equatorial undercurrent is forecast in all scenarios. In most cases, these changes are more extreme in the scenarios with higher greenhouse gases emissions and more significant climate change. The mean rise in temperature is between 0.9 \degree C and 1.2 \degree C over the first half of the 21st century. The integrated primary production and nutrients are forecast to decline in the MPA, but there is less consistency between models in projections of salinity, surface chlorophyll, and dissolved oxygen concentration at 500m depth. The combined effects of these projections may lead to changes in ecosystem services around Ascension Island. The effects of the model outputs were interpreted for three key ecosystem service providing habitats: biogenic deep sea habitats, intertidal sand and intertidal rocky shores. The outcomes were then used to assess potential effects on eight marine and coastal ecosystem services and information was compared to current ecosystem service levels.
A novel, multi-scale climate modeling approach is used to provide evidence of potential increases in tornado intensity due to anthropogenic climate change. Historical warm- and cool-season (WARM and COOL) tornado events are virtually placed in a globally warmed future via the “pseudo-global warming” method. As hypothesized based on meteorological arguments, the tornadic-storm and associated vortex of the COOL event experiences consistent and robust increases in intensity, size, and duration in an ensemble of imposed climate-change experiments. The tornadic-storm and associated vortex of the WARM event experiences increases in intensity in some of the experiments, but the response is neither consistent nor robust, and is overall weaker than in the COOL event. An examination of environmental parameters provides further support of the disproportionately stronger response in the cool-season event. These results have implications on future tornadoes forming outside of climatologically favored seasons.