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Increasing concentrations of greenhouse gases in the atmosphere are expected to modify the global water cycle with significant consequences for terrestrial hydrology. We assess the impact of climate change on hydrological droughts in a multimodel experiment including seven global impact models (GIMs) driven by bias-corrected climate from five global climate models under four representative concentration pathways (RCPs). Drought severity is defined as the fraction of land under drought conditions. Results show a likely increase in the global severity of hydrological drought at the end of the 21st century, with systematically greater increases for RCPs describing stronger radiative forcings. Under RCP8.5, droughts exceeding 40% of analyzed land area are projected by nearly half of the simulations. This increase in drought severity has a strong signal-to-noise ratio at the global scale, and Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia are identified as possible hotspots for future water security issues. The uncertainty due to GIMs is greater than that from global climate models, particularly if including a GIM that accounts for the dynamic response of plants to CO ₂ and climate, as this model simulates little or no increase in drought frequency. Our study demonstrates that different representations of terrestrial water-cycle processes in GIMs are responsible for a much larger uncertainty in the response of hydrological drought to climate change than previously thought. When assessing the impact of climate change on hydrology, it is therefore critical to consider a diverse range of GIMs to better capture the uncertainty.

A suite of river discharge, tree-ring, sedimentary and climate data shows that the Mississippi’s flood magnitude has risen by about twenty per cent over the past half-century, largely owing to engineering works. Engineering magnifies Mississippi's rising rivers Instrumental records of river discharge do not go far enough back in time to place recent flood activity in a longer-term context, making it difficult to understand how climate variability and human activity might have affected flooding. Now, Samuel Munoz and colleagues reconstruct the past flood frequency of the Mississippi River from a compilation of river-discharge, tree-ring, sedimentary and climate data. The results show that the magnitude of the 100-year flood has gone up by about 20 per cent over the past 500 years. Climate cycles account for most of the variability in flooding on multidecadal timescales, but engineering works account for about three-quarters of the long-term increase. Over the past century, many of the world’s major rivers have been modified for the purposes of flood mitigation, power generation and commercial navigation 1 . Engineering modifications to the Mississippi River system have altered the river’s sediment levels and channel morphology 2 , but the influence of these modifications on flood hazard is debated 3 , 4 , 5 . Detecting and attributing changes in river discharge is challenging because instrumental streamflow records are often too short to evaluate the range of natural hydrological variability before the establishment of flood mitigation infrastructure. Here we show that multi-decadal trends of flood hazard on the lower Mississippi River are strongly modulated by dynamical modes of climate variability, particularly the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation, but that the artificial channelization (confinement to a straightened channel) has greatly amplified flood magnitudes over the past century. Our results, based on a multi-proxy reconstruction of flood frequency and magnitude spanning the past 500 years, reveal that the magnitude of the 100-year flood (a flood with a 1 per cent chance of being exceeded in any year) has increased by 20 per cent over those five centuries, with about 75 per cent of this increase attributed to river engineering. We conclude that the interaction of human alterations to the Mississippi River system with dynamical modes of climate variability has elevated the current flood hazard to levels that are unprecedented within the past five centuries.

Wind-generated waves at the sea surface are of outstanding importance for both their practical relevance in many aspects, such as coastal erosion, protection, or safety of navigation, and for their scientific relevance in modifying fluxes at the air–sea interface. So far, long-term changes in ocean wave climate have been studied mostly from a regional perspective with global dynamical studies emerging only recently. Here a global wave climate study is presented, in which a global wave model [Wave Ocean Model(WAM)] is driven by atmospheric forcing from a global climate model (ECHAM5) for present-day and potential future climate conditions represented by the Intergovernmental Panel for Climate Change (IPCC) A1B emission scenario. It is found that changes in mean and extreme wave climate toward the end of the twenty-first century are small to moderate, with the largest signals being a poleward shift in the annual mean and extreme significant wave heights in the midlatitudes of both hemispheres, more pronounced in the Southern Hemisphere and most likely associated with a corresponding shift in midlatitude storm tracks. These changes are broadly consistent with results from the few studies available so far. The projected changes in the mean wave periods, associated with the changes in the wave climate in the middle to high latitudes, are also shown, revealing a moderate increase in the equatorial eastern side of the ocean basins. This study presents a step forward toward a larger ensemble of global wave climate projections required to better assess robustness and uncertainty of potential future wave climate change.

Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures.

We present four 140-yr wind-wave climate simulations (1961–2100) forced with surface wind speed and sea ice concentration from two CMIP6 GCMs under two different climate scenarios: SSP1–2.6 and SSP5–8.5. A global three-grid system is implemented in WAVEWATCH III to simulate the wave–ice interactions in the Arctic and Antarctic regions. The models perform well in comparison with global satellite altimeter and in situ buoys climatology. The comparison with traditional trend analyses demonstrates the present GCM-forced wave models’ ability to reproduce the main historical climate signals. The long-term datasets allow a comprehensive description of the twentieth- and twenty-first-century wave climate and yield statistically robust trends. Analysis of the latest IPCC ocean climatic regions highlights four regions where changes in wave climate are projected to be most significant: the Arctic, the North Pacific, the North Atlantic, and the Southern Ocean. The main driver of offshore wave climate change is the wind, except for the Arctic where the significant sea ice retreat causes a sharp increase in the projected wave heights. Distinct changes in the wave period and the wave direction are found in the Southern Hemisphere, where the poleward shift of the Southern Ocean westerlies causes an increase in the wave period of up to 5% and a counterclockwise change in wave direction of up to 5°. The new CMIP6 forced wave models improve in performance compared to previous CMIP5 forced wave models, and will ultimately contribute to a new CMIP6 wind-wave climate model ensemble, crucial for coastal adaptation strategies and the design of future marine offshore structures and operations.

How drought may change in the future are of great concern as global warming continues. In Part I of this study, we examine the uncertainties in estimating recent drought changes. Substantial uncertainties arise in the calculated Palmer Drought Severity Index (PDSI) with Penman-Monteith potential evapotranspiraiton (PDSI_pm) due to different choices of forcing data (especially for precipitation, solar radiation and wind speed) and the calibration period. After detailed analyses, we recommend using the Global Precipitation Climatology Centre (GPCC) or the Global Precipitation Climatology (GPCP) datasets over other existing land precipitation products due to poor data coverage in the other datasets since the 1990s. We also recommend not to include the years after 1980 in the PDSI calibration period to avoid including the anthropogenic climate change as part of the natural variability used for calibration. Consistent with reported declines in pan evaporation, our calculated potential evapotranspiration (PET) shows negative or small trends since 1950 over the United States, China, and other regions, and no global PET trends from 1950 to 1990. Updated precipitation and streamflow data and the self-calibrated PDSI_pm all show consistent drying during 1950–2012 over most Africa, East and South Asia, southern Europe, eastern Australia, and many parts of the Americas. While these regional drying trends resulted primarily from precipitation changes related to multi-decadal oscillations in Pacific sea surface temperatures, rapid surface warming and associated increases in surface vapor pressure deficit since the 1980s have become an increasingly important cause of widespread drying over land.

Climate change is a major factor driving shifts in the distribution of invasive pests. The oriental fruit fly, Bactrocera dorsalis, native to mainland Asia, has spread throughout Southeast Asia and sub-Saharan Africa. Recently, the species has extended its Asian range northward into regions previously thought unsuitable which presents a major new risk to temperate zone agriculture and has invaded Italy. Thus, it is necessary to study how climate change may impact on the global distribution of B. dorsalis. MaxEnt models were used to map suitable habitat for this species under current and future climate conditions averaged from four global climate models under two representative emission pathways in 2050 and 2070. The results highlighted that a total of 30.84% of the world’s land mass is currently climatically suitable including parts of the western coast and southeast of the USA, most of Latin America, parts of Mediterranean coastal European regions, northern and coastal Australia, and the north island of New Zealand. Under future climate conditions, the risk area of B. dorsalis in the northern hemisphere was projected to expand northward, while in the southern hemisphere, it would be southward, especially by 2070 under RCP85 with very high greenhouse gas emissions. Future management of this pest should consider the impacts of the global climate change on its potential geographical distribution.

Karst regions offer a variety of natural resources such as freshwater and biodiversity, and many cultural resources. The World Karst Aquifer Map (WOKAM) is the first detailed and complete global geodatabase concerning the distribution of karstifiable rocks (carbonates and evaporites) representing potential karst aquifers. This study presents a statistical evaluation of WOKAM, focusing entirely on karst in carbonate rocks and addressing four main aspects: (1) global occurrence and geographic distribution of karst; (2) karst in various topographic settings and coastal areas; (3) karst in different climatic zones; and (4) populations living on karst. According to the analysis, 15.2% of the global ice-free continental surface is characterized by the presence of karstifiable carbonate rock. The largest percentage is in Europe (21.8%); the largest absolute area occurs in Asia (8.35 million km2). Globally, 31.1% of all surface exposures of carbonate rocks occur in plains, 28.1% in hills and 40.8% in mountains, and 151,400 km or 15.7% of marine coastlines are characterized by carbonate rocks. About 34.2% of all carbonate rocks occur in arid climates, followed by 28.2% in cold and 15.9% in temperate climates, whereas only 13.1 and 8.6% occur in tropical and polar climates, respectively. Globally, 1.18 billion people (16.5% of the global population) live on karst. The highest absolute number occurs in Asia (661.7 million), whereas the highest percentages are in Europe (25.3%) and North America (23.5%). These results demonstrate the global importance of karst and serve as a basis for further research and international water management strategies.

Drought is a climate risk that affects access to safe water, crop development, ecological stability, and food production. Therefore, developing drought prediction methods can lead to better management of surface and groundwater resources. Similarly, machine learning can be used to find improved relationships between nonlinear variables in complex systems. Initially, the standardized precipitation evapotranspiration index (SPEI) was calculated, and then using large–scale signals such as large–scale climate signals (the North Atlantic Oscillation, the Arctic Oscillation, the Pacific Decadal Oscillation, and the Southern Oscillation Index), along with climatic variables including temperature, precipitation, and potential evapotranspiration, predictions were made for the period of 1966–2014. Several new machine learning models including Least Square Support Vector Regression (LSSVR), Group Method of Data Handling (GMDH), and Multivariate Adaptive Regression Splines (MARS) were used for prediction. The results showed that in estimating SPEI in moderately arid climates, the GMDH model with criteria (RMSE = 0.26, MAE = 0.17, NSE = 0.95 in validation) under scenario S1 (included all variables plus the SPEI of the previous month) performed better, while in arid and cold climates, the LSSVR model (RMSE = 0.22, MAE = 0.18, NSE = 0.95 in validation) under S1, and in arid and hot climate, the LSSVR model (RMSE = 0.29, MAE = 0.19, NSE = 0.93 in validation) under scenario S2 (included meteorological variables plus the SPEI of the previous month) had higher prediction accuracy. Although the MARS model was less accurate in validation, it showed higher accuracy during calibration compared to the other two models in all climates. The results showed that using large–scale signals for predicting SPEI was beneficial. It can be concluded that machine learning models are useful tools for predicting the SPEI drought index in different climates within similar ranges.