The monsoons in Pakistan have been exceptionally harsh in recent decades, resulting in extraordinary drought conditions and record flooding events. The changing frequency of extreme events is widely attributed to climate change. However, given this region's long history of floods and droughts, the role of natural climate variability cannot be rejected without a careful diagnosis. Here, we examine how oceanic and atmospheric variability has contributed to unusual precipitation distributions in West South Asia. Variations in sea surface temperatures in the tropical Pacific and northern Arabian Sea, and internal atmospheric variability related to the circumglobal teleconnection pattern and the subtropical westerly jet stream, explain more than 70% of monthly summer precipitation variability in the 21st century. Several of these forcings have co-occurred with record strength during episodes of extreme monsoons, which have exacerbated the overall effect. Climate change may have contributed to increased variability and the in-phase co-occurrences of the identified mechanisms, but further research is required to confirm any such connection.
The origins of river and floodplain waters (groundwater, rainfall, and snowmelt) and their extent during overbank flow events strongly impact ecological processes such as denitrification and vegetation development. However, the long-term sensitivity of floodplain water signatures to climate change remains elusive. We examined how the integrated hydrological model HydroGeoSphere and the Hydraulic Mixing-Cell method could help us understand the long-term impact of climate change on water signatures and their spatial distribution in the protected Biebrza River Catchment in northeastern Poland. Our model relied on 20th century Reanalysis Data from 1881 to 2015 and an ensemble of EURO-CORDEX simulations for RCP 2.6, 4.5, and 8.5 from 2006 to 2099. The historical component of the simulations was subjected to extensive multiple-variable validation from 1881 to 2019. The results show that the extents of water sources were rather stable in the floodplain in the 1881-2015 period. The projected future impacts were variable with each analyzed RCP, but in all cases, different significant trends were present for the spatial distribution of water sources and for the river-floodplain mixing. However, the total volume of water from different sources was less sensitive to climate change than the dominant sources and spatial distribution of water. The simulation results highlight the impact of climate change on the extent of water sources in temperate zone wetlands with significant implications for ecological processes and management. These results also underscore the urgent need to leverage such modeling studies to inform protective and preservation strategies of floodplain wetlands.
To aid California's water sector to better manage future climate extremes, we present a method for creating a regional ensemble of plausible daily future climate and streamflow scenarios that represent natural climate variability captured in a network of tree-ring chronologies, and then embed anthropogenic climate change trends within those scenarios. We use 600 years of paleo-reconstructed weather regimes to force a stochastic weather generator, which we develop for five subbasins in the San Joaquin River in the Central Valley region of California. To assess the compound effects of climate change, we create temperature series that reflect scenarios of warming and precipitation series that are scaled to reflect thermodynamically driven shifts in the daily precipitation distribution. We then use these weather scenarios to force hydrologic models for each of the San Joaquin subbasins. The paleo-forced streamflow scenarios highlight periods in the region's past that produce flood and drought extremes that surpass those in the modern record and exhibit large non-stationarity through the reconstruction. Variance decomposition is employed to characterize the contribution of natural variability and climate change to variability in decision-relevant metrics related to floods and drought. Our results show that a large portion of variability in individual subbasin and spatially compounding extreme events can be attributed to natural variability, but that anthropogenic climate changes become more influential at longer planning horizons. The joint importance of climate change and natural variability in shaping extreme floods and droughts is critical to resilient water systems planning and management in the Central Valley region.
Rising temperatures amplify biogenic volatile organic compound (VOC) emissions from arctic vegetation, causing feedbacks to the climate system. Changes in climate also alter plant physiology and vegetation composition, all of which can influence VOC emissions. Moreover, leaf development and biotic stresses cause highly variable emissions during the growing season. Therefore, linking VOC emissions with plant traits and tracking responses to climate change might provide better understanding of VOC emission regulation under future conditions. We measured VOC emissions and other plant traits in dwarf birch (Betula glandulosa) at two elevations in Narsarsuaq, South Greenland. The measurements were performed in warming experiments that have run since 2016. We collected VOCs using the branch enclosure method from early June until late July 2019 (n = 200). Emissions of green leaf volatiles (GLVs), oxygenated monoterpenes (oMTs), and homoterpenes followed a seasonal trend. VOC emission rates and the diversity of the VOC blend decreased at the end of the measurement period. Differences in VOC emission rates between elevations were most pronounced early in the season. Most traits did not explain the variation in VOC emissions. We show strong seasonal variability in VOC emissions within the growing season, which are likely driven by leaf phenology. While the diversity of VOCs was greater at the milder low-elevation site, VOC emission rates were higher or similar at the harsher high-elevation site, showing stronger VOC emission potentials than previously assumed. Seasonal variations in the VOCs are crucial for accurate predictions of current and future VOC emissions from arctic ecosystems.
The oceans are acidifying in response to the oceanic uptake of anthropogenic CO2 from the atmosphere, yet the global-scale progression of this acidification has been poorly documented so far by observations. Here, we fill this gap and use an observation-based product, OceanSODA-ETHZ, to determine the trends and drivers of the surface ocean aragonite saturation state (Ωar) and pH over the last four decades (1982-2021). In the global mean, Ωar and pH declined at rates of -0.071 ± 0.001 decade-1 and -0.0170 ± 0.0001 decade-1, respectively. These trends are driven primarily by the increase in the surface ocean concentration of dissolved inorganic carbon (DIC) in response to the uptake of anthropogenic CO2 but moderated by changes in natural DIC. Surface warming enhances the decrease in pH, accounting for ∼15% of the global trend. Substantial ENSO-driven interannual variability is superimposed on these trends, with Ωar showing greater variability than pH.
Glaciers and perennial snowfields are important to alpine ecosystems and regional hydrology. Quantifying volume change of a population of glaciers widely distributed over a region is difficult and expensive. We employed NASA’s novel Airborne Glacier and Ice Surface Topography Interferometer (GLISTIN) to rapidly map surface topography of alpine glaciers across the western USA. In five flight days 3289 glaciers and perennial snowfields were surveyed. Comparison with lidar over control sites showed a mean difference of +0.17 ±1.78 m at a spatial scale of 3 m. Data coverage increased and elevation uncertainty decreased with the mosaicking of multiple passes due to the complex terrain. Elevation change since the National Elevation Dataset shows a thinning (and volume loss) over the last ~56 years, averaging -0.3 ± 0.2 m and accelerating since 1980. GLISTIN can be a valuable tool for rapidly mapping ice surfaces in the alpine environment.
Equilibrium climate sensitivity - ECS - is easily-understood, has been studied for over 150 years and is therefore appealing as a metric for communication of climate model results. In this work I argue that ECS is not a good metric for comparing different climate models. Via brief examples concerning the Pliocene epoch and the Paleocene-Eocene Thermal Maximum , I further argue that models which produce temperatures towards the higher end of model intercomparisons are useful in spite of recent studies concluding that these models are 'too hot'. I hope that this brief manuscript generates discussion on how to prioritise the consideration of more useful, and potentially novel, ways of comparing climate models going forward.
Contributions from fossil fuel companies to a Loss and Damage fund have been explicitly called for. Here we estimate societal damages caused by emissions attributable to fossil fuel companies to be of the order of several trillion USD since 1985 and to exceed the cumulative company profits generated over the same period. Even record profits of 2022 do not match the annually inflicted climate damages.
Earth System Models (ESMs) are the primary tool for understanding the impacts of global change and several ESMs are updated on a regular basis to provide more reliable scenarios of the future. However, the confrontation of ESMs outputs to observations reveals biases that are important to correct, especially for impact applications where the absolute scale of the environmental variable is as relevant as its trends. In addition, regional impact studies need fine scale projections to devise strategic planning and management measures. Statistical downscaling provides a fast way to produce regional ocean forcing from ESMs and can additionally produce bias-corrected outputs, which are necessary for impact applications driven by or fitted to observed data, like many ecological models. Statistical downscaling can make use of different parametric distributions depending on the variables used, and generalized regression can provide a flexible approach for this purpose. We propose a multi-model approach based on non-parametric generalized regression and a suite of indicators to select a robust statistical downscaling model that can be used for projection of future scenarios. The empirical cumulative distribution of the variables to downscale is modeled, ensuring that not only the mean but also the variance and quantiles (including the minima and maxima) are properly represented, improving the prediction of extreme events and taking into account spatial autocorrelation. The approach presented here is applied to two contrasted regional case studies, the Bay of Biscay-Celtic Sea ecosystem and the Northern Peru Current ecosystem, using the Sea Surface Temperature from the IPSL-CM5A-LR ESM. The results showed that a multi-model selection approach is appropriate as individual model performance is case specific.
This study provides a global analysis of drought metrics obtained from several climatic, hydrologic and ecological variables in a climate change framework using CMIP6 model data. A comprehensive analysis of the evolution of drought severity on a global scale is carried out for the historical experiment (1850-2014) and for future simulations under a high emissions scenario (SSP5-8.5). This study focuses on assessing trends in the magnitude and duration of drought events according to different standardised indices over the world land-surface area. The spatial and temporal agreement between the different drought indices on a global scale was also evaluated. Overall, there is a fairly large consensus among models and drought metrics in pointing to drought increase in southern North America, Central America, the Amazon region, the Mediterranean, southern Africa and southern Australia. Our results show important spatial differences in drought projections, which are highly dependent on the drought metric employed. While a strong relationship between climatic indices was evident, climatic and ecological drought metrics showed less dependency over both space and time. Importantly, our study demonstrates uncertainties in future projections of drought trends and their interannual variability, stressing the importance of coherent hydrological and plant physiological patterns when analysing CMIP6 model simulations of droughts under a warming climate scenario.
We used a spatially distributed and physically based energy and mass balance model to derive the Østrem curve, that is the supraglacial debris-related relative melt alteration versus the debris thickness, for the Djankuat Glacier, Caucasus, Russian Federation. The model is driven by meteorological input data from two on-glacier automatic weather stations and ERA-5 reanalysis data. A direct pixel-by-pixel comparison of the melt rates obtained from both a clean ice and debris-covered ice mass balance model results in the quantification of debris-related relative melt-modification ratios, capturing the degree of melt enhancement or suppression as a function of the debris thickness. In doing so, our model is the first attempt to derive the glacier-specific Østrem curve through spatially distributed energy and mass balance modelling. The main results show that a maximum relative melt enhancement occurs on the Djankuat Glacier for thin and patchy debris with a thickness of 3 cm. However, insulating effects suppress sub-debris melt under debris layers thicker than a critical debris thickness of 9 cm. Sensitivity experiments show that especially within-debris properties, such as the thermal conductivity, the vertical porosity gradient and the moisture content of the debris pack, highly impact the magnitude of the sub-debris melt rates. The Østrem curve is also shaped by the local climate. Our results highlight the need to account for site-specific debris properties and their variation with depth, as well as for the effects of changing local climatic conditions in order to accurately assess (partly) debris-covered glacier behavior and its climate change response.
Using the Alfred Wegener Institute Climate Model (AWI-CM 1.1 LR), we conduct sensitivity experiments separating the Arctic and extra-Arctic warming to investigate the transient response of AMOC to quadrupled carbon dioxide (4×CO2) forcings. The results suggest that AMOC weakening is primarily affected by circulation adjustment induced by the outer-Arctic warming, while the effects of Arctic warming are confined to the polar range and contribute less to AMOC changes. When warming forcing is applied outside the Arctic, the increases of northward advective heat transport dominate the weakening of deep convection in the Nordic Seas, while the reduction of heat loss from ocean to atmosphere is prevalent in Labrador Sea. Besides, the weakening of deep convection in Nordic Seas is more pronounced than in Labrador Sea, implying a leading role of Nordic Seas in the weakening of AMOC under global warming.
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