Decrease In Precipitation Research Articles

A 900-Year Isotopic Proxy Rainfall Record from Northeastern Botswana

A high-resolution climate archive was reconstructed based on carbon isotope analysis and radiocarbon dating of the Chapman baobab in northeastern Botswana. The Chapman baobab, which exhibited an open ring-shaped structure composed of six stems, collapsed in January 2016 during an intense El Niño event. Two samples belonging to the oldest stems were investigated in order to obtain a proxy rainfall record, which provides insight into the precipitation regime over the last millennium, evincing centennial and decadal scale variability. The results indicate that the Medieval Warm Period was marked by relatively stable precipitation, whereas rainfall variability and drought frequency increased during the Little Ice Age. The investigated area has experienced both wetter and drier conditions in the past. The wettest conditions of the last millennium were registered before 1450 while the driest period occurred in 1835. For southern Africa, inter-annual rainfall variability is mainly associated with sea surface temperatures in the Agulhas Current core region, which determine the east–west displacement of tropical temperate troughs. Previous studies suggested that positive sea surface temperature anomalies in the Mozambique Channel led to an eastward movement of the troughs but the Chapman record demonstrates a westward displacement in the past, causing drought in northeastern South Africa and wetter conditions in the central part of southern Africa. The positive rainfall correlation with SST anomalies reversed after 1900, causing a gradual decrease in precipitation and confirming the current aridity trend for Botswana. The results contribute to a better understanding of the past climate of southern Africa for which paleoclimate reconstructions remain scarce.

A comprehensive assessment of RCP4.5 projections and bias-correction techniques in a complex coastal karstic aquifer in the Mediterranean

This study aims to assess the projected precipitation and temperature changes at the coastal karstic aquifer of Salento (Apulia, Southern Italy) under the Representative Concentration Pathway RCP4.5. For this purpose, an ensemble of twelve Regional Climate Models (RCMs) driven by several General Circulation Models (GCMs) were collected. Eight bias-correction (BC) methods were applied at daily time steps, and their results were assessed on monthly and annual time steps, using daily records from 19 and 11 precipitation and temperature (minimum and maximum) stations, respectively, for the period 1960–2005. Missing data in the observed dataset were filled-in applying the best performing techniques out of the 5 that were employed and tested. The Linear Scaling and the Power Transformation were found to be the most effective methods for precipitation BC at the case study, while all methods performed equally well in correcting air temperature datasets. Results of future climate projections show a decrease in precipitation of about 6% and an increase in temperature of 2°C until the end of this century, compared to the historical period (1971–2005). This study forms the first comprehensive attempt to test the scientific literature’s most widely used bias-correction methods over the study area. The case study may be considered a benchmark for circum-Mediterranean regions because of its high geomorphological and structural complexity, regional size, surface water scarcity, and significant water withdrawals for human activities.

Dependence of Fast Changes in Global and Local Precipitation on the Geographical Location of Absorbing Aerosol

Abstract Anthropogenic aerosol interacts strongly with incoming solar radiation, perturbing Earth’s energy budget and precipitation on both local and global scales. Understanding these changes in precipitation has proven particularly difficult for the case of absorbing aerosol, which absorbs a significant amount of incoming solar radiation and hence acts as a source of localized diabatic heating to the atmosphere. In this work, we use an ensemble of atmosphere-only climate model simulations forced by identical absorbing aerosol perturbations in different geographical locations across the globe to develop a basic physical understanding of how this localized heating impacts the atmosphere and how these changes impact on precipitation both globally and locally. In agreement with previous studies we find that absorbing aerosol causes a decrease in global-mean precipitation, but we also show that even for identical aerosol optical depth perturbations, the global-mean precipitation change varies by over an order of magnitude depending on the location of the aerosol burden. Our experiments also demonstrate that the local precipitation response to absorbing aerosol is opposite in sign between the tropics and the extratropics, as found by previous work. We then show that this contrasting response can be understood in terms of different mechanisms by which the large-scale circulation responds to heating in the extratropics and in the tropics. We provide a simple theory to explain variations in the local precipitation response to absorbing aerosol in the tropics. Our work highlights that the spatial pattern of absorbing aerosol and its interactions with circulation are a key determinant of its overall climate impact and must be taken into account when developing our understanding of aerosol–climate interactions.

Opposite Changes in Monsoon Precipitation and Low Pressure System Frequency in Response to Orographic Forcing

Abstract Monsoon low pressure systems (LPS) are synoptic-scale disturbances that form along the quasi-stationary trough of the larger-scale South Asian summer monsoon, producing a large fraction of total monsoon precipitation. Here, we use an Earth system model to investigate the influence of Tibetan and Himalayan orography (THO) on mean monsoon strength and LPS activity. The influence of THO height on LPS activity has not been investigated before. The model simulates decreased mean monsoon circulation and precipitation when THO is removed, but the number of LPS and the meridional extent of LPS activity increase; this is an unexpected, important finding given that LPS are principal rain-bearing systems of the monsoon. The decreased mean monsoon circulation and precipitation on removal of THO are attributed to enhanced mixing of dry extratropical air into the humid monsoon domain, as demonstrated by prior studies. The increase in LPS frequency and the meridional extent of LPS activity is attributed to the increase in magnitude and meridional extent of the low-level meridional shear of zonal wind, which previous work showed amplifies LPS through barotropic growth. Specifically, as monsoon precipitation decreases, the sensible heat-driven low-level trough intensifies and shifts equatorward; this strengthens the shear zone in which LPS grow. Conversely, increasing THO height decreases the magnitude and meridional extent of cyclonic shear over India, decreasing LPS frequency and the spatial extent of LPS activity while increasing total monsoon precipitation. These results demonstrate that LPS activity and total monsoon rainfall can undergo large, opposing changes in response to imposed forcings. Significance Statement Monsoon low pressure systems (LPS) are propagating atmospheric vortices that deliver a large fraction of seasonal-mean precipitation to agricultural regions of India. The influence of Tibetan and Himalayan orography (THO) on the South Asian monsoon is an active area of research, but its effect on LPS has remained unexplored. Here, a global climate model is used to simulate LPS characteristics for different heights of THO. Flattening the THO reduces total summer monsoon precipitation but increases the number of LPS and the spatial extent of LPS activity. We attribute these LPS changes to a decrease in the magnitude and meridional extent of the hydrodynamically unstable low-level cyclonic shear zone over South Asia that occurs when orography is flattened.

An Overview of the Impacts of Climate Change on Vineyard Ecosystems in Niagara, Canada

Vineyards are agroecosystems of great importance in the Niagara Region, Ontario (Canada). Due to its microclimate, this region is projected to be impacted by climate change with temperature increases, changes in precipitation patterns in all seasons, and greater frequency of extreme weather events. The aim of this review paper is to summarize which seasonal changes are expected to occur in the Niagara Region and assess how such changes are likely to affect the main components of the vineyard ecosystem (i.e., soil, vines, invertebrates, and pathogens). It is expected that by 2080 the region will experience an increase in temperature in all four seasons; an increase in precipitation during the fall, winter, and spring; and a decrease in precipitation during summer months. Impacts of the projected changes will likely lead to vine water stress, yield loss, increases in incidents of diseases, increases in the spread of new pests, and changes in grape quality ultimately resulting in lower wine quality and/or production. Current management practices will need to be better understood and adaptive strategies introduced to enhance grape growers’ ability to minimize these impacts.

Climate of Syria Based on Cordex Simulations: Present and Future

AbstractRegional climate models are widely used to assess current and future impacts of climate change. In this study, we evaluate the performance of regional climate models from the Coordinated Regional Climate Downscaling Experiment programme integrated over the following three CORDEX domains: AFR, MNA and WAS. Four meteorological variables (temperature, precipitation, solar radiation and cloud cover) were evaluated over Syria at a grid spacing of 0.44°. The performance of five models in simulating the present climate characteristics (1989–2008) is evaluated with respect to the observations: CRU, ERA5 reanalysis and SARA and CLARA satellite data. We find that the mini-ensemble captures well the general spatial patterns and annual cycles of the selected variables. Anotheraim of this study was to assess the expected change of the mentioned four climate variables over Syria under the moderate emission scenario (RCP4.5) and the high emission scenario (RCP8.5) in the near future (2031–2050) and in the far future (2080–2099) with respect to the present climate (1989–2008). The simulations show a decreasing trend in cloud cover (between 6% and 10%) and precipitation (up to 9%) by mid and late century, regardless of the forcing scenarios. The simulations show a pronounced warming over Syria, which is expected to reach 6 °C by the end of the twenty-first century following the high greenhouse gas concentration scenario (RCP8.5). Furthermore, such an increase, combined with a decrease in precipitation, will shift Syria’s climate towards a more arid one.

Uncertainty Quantification of Deep Learning–Based Statistical Downscaling of Climatic Parameters

Abstract This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty.

Влияние гелиометеорологических факторов на эпидемический процесс геморрагической лихорадки с почечным синдромом и прогнозирование заболеваемости на территории лесостепного и степного ландшафтов

On the territory of the Orenburg region over a 25-year period, we studied the influence of 43 helio-meteorological factors on the cumulative incidence of hemorrhagic fever with renal syndrome (HFRS) in the population of the forest-steppe and steppe landscapes. It is shown that in of the forest-steppe landscape the priority helio-meteorological risk factors for the incidence of HFRS were: an increase in precipitation in March of the current year and in October of the previous year, as well as an increase in the Wolf number with a shift relative to the incidence to the left by 6 years. On the territory of the steppe landscape, the leading helio-meteorological risk factors for the epidemic process of HFRS were: a decrease in precipitation in June of the previous year, an increase in precipitation in February of the current year, and an increase in the Wolf number with a shift to the left relative to the incidence by 3 years. We have obtained regression equations for a short-term (for the current year) forecast of the intensity of the epidemic process of HFRS, taking into account helio-meteorological factors in the territory of the forest-steppe and steppe landscapes. These mathematical equations have been tested against actual morbidity rates. They can be recommended for practical use, as well as further improvement of the model. Keywords: hemorrhagic fever with renal syndrome, forest-steppe and steppe landscapes, prognosis, incidence.

СООТНОШЕНИЕ ТЕПЛА И ВЛАГИ В СЕЛЬСКОХОЗЯЙСТВЕННЫХ РАЙОНАХ ЮГА РОССИИ В ЛЕТНИЙ ПЕРИОД ПРИ ИЗМЕНЕНИИ КЛИМАТА

It is revealed that there was a significant increase in the Bowen ratio in the Volga, Southern, and North Caucasian federal districts in the second half of the 20th century-beginning of the 21st century. This indicates a strengthening of sensible heat flux and a reduction of latent heat flux in the heat and moisture balance. These changes are caused by a significant temperature rise, especially in the south of European Russia, with a regional decrease in precipitation. The Bowen ratio is inversely proportional to precipitation and NDVI and directly proportional to temperature. In the steppe zone represented by the Volga, Southern, and North Caucasian federal districts the long periods of predominance of the Bowen ratio anomalies of a certain sign can be distinguished for June and July: the positive ones in the 1950s and until the mid-1960s, the negative ones after that until the early 2000s, and the positive ones since the middle of the first decade of the 21st century. Since the second decade of the 21st century, the positive anomalies of the Bowen ratio have become more frequent in some years and have exceeded the standard deviation. An increase in the contribution of heat to the heat and moisture exchange balance led to a degradation of vegetation conditions in the Volga, Southern, and North Caucasus federal districts. In the south of the Siberian Federal District, the fluctuation of the Bowen ratio anomalies is less pronounced than in European Russia, which suggests some stability of heat and moisture supply in summer over the past 70 years.

A clustering-based multi-model ensemble projection of near-term precipitation changes over East China and its uncertainty

An ensemble of 28 models from the 6th Coupled Model Intercomparison Project was used to project future changes in annual mean precipitation over East China during 2046–2065 relative to 1995–2014 under the SSP2-4.5 scenario. A precipitation increase of 0.16 mm d−1 is projected over North China by the multi-model ensemble (MME) mean. However, large model uncertainty exists over South China (SC), reducing the fidelity of the MME mean projection. Thus, a clustering-based MME probabilistic projection is presented that projects four possible SC precipitation changes in the future. Precipitation increases are projected in Cluster 1, Cluster 3 and Cluster 4 for 0.51 mm d−1, 0.2 mm d−1 and 0.23 mm d−1, respectively, with occurrence probabilities of 14.3%, 35.7% and 25%, respectively. Conversely, the projected Cluster 2 precipitation decrease is 0.01 mm d−1 with an occurrence probability of 25%. The differences in precipitation change are mainly contributed by dynamic effect due to different circulation changes across clusters. During extended summer, different circulation anomalies over western North Pacific (WNP) among clusters arise from the sea surface temperature anomaly (SSTA) warming patterns over the equatorial central-eastern Pacific, which explain the different precipitation increases over SC. During extended winter, a strong zonal SSTA gradient between the South China Sea and adjacent WNP is projected in Cluster 2, stimulating a zonally vertical cell with anomalous descent over SC and resulting in markedly decreased precipitation. A similar but much weaker zonal SSTA gradient and circulation anomaly are projected in Cluster 3. Distinct meridional SSTA gradients over the WNP are projected in the rest clusters, stimulating shifted descents with a weak effect on SC precipitation.

Warming temperatures exacerbate groundwater depletion rates in India.

Climate change will likely increase crop water demand, and farmers may adapt by applying more irrigation. Understanding the extent to which this is occurring is of particular importance in India, a global groundwater depletion hotspot, where increased withdrawals may further jeopardize groundwater resources. Using historical data on groundwater levels, climate, and crop water stress, we find that farmers have adapted to warming temperatures by intensifying groundwater withdrawals, substantially accelerating groundwater depletion rates in India. When considering increased withdrawals due to warming, we project that the rates of net groundwater loss for 2041-2080 could be three times current depletion rates, even after considering projected increases in precipitation and possible decreases in irrigation use as groundwater tables fall. These results reveal a previously unquantified cost of adapting to warming temperatures that will likely further threaten India's food and water security over the coming decades.

Multidecadal Oceanic Modulation of Summer Precipitation in North China in 1200-Year Global Climate Simulations

Abstract The multidecadal decrease of precipitation in North China since the 1960s has caused a significant impact on the ecological environment and social development. It has become a key concern for meteorologists and national decision-makers to understand what is driving this. To better understand the oceanic impact on the interdecadal variation of summer precipitation in North China, we analyze two 1200-yr global climate simulations from state-of-the-art coupled models with an explainable convolutional neural network (CNN). The accuracy of the CNN model in correctly diagnosing the increased and decreased summer precipitation anomaly in North China reaches 98% and 96.6% in two CMIP6 models, respectively. We find that a sea surface temperature anomaly (SSTA) in the northern Pacific, associated with the Pacific decadal oscillation (PDO), and an SSTA in the northern Atlantic, associated with the Atlantic multidecadal oscillation (AMO), play essential roles in the interdecadal variation of summer precipitation in North China. The modulations of the PDO and AMO on summer precipitation in North China are consistent with the results derived from limited observations and simulations, which means these mechanisms are robust. In particular, when the PDO is in a negative phase and the AMO is in a positive phase, the positive anomaly of precipitation and the associated circulation anomalies are intensified. This study also confirms that with the explainable deep learning method, a convolutional neural network can be used to identify multidecadal variability in North China summer precipitation, although limited datasets such as observational records make it more challenging to apply deep learning methods.

Stable isotopes contain substantial additive information about terrestrial carbon and water cycling

Stable isotope ratios of H (δ 2 H), O (δ 18O), and C (δ 13C) are linked to key biogeochemical processes of the water and carbon cycles; however, the degree to which isotope-associated processes are reflected in macroscale ecosystem flux observations remains unquantified. Here through formal information assessment, new measurements of δ 13C of net ecosystem exchange (NEE) as well as δ 2H and δ 18O of latent heat (LH) fluxes across the United States National Ecological Observation Network (NEON) are used to determine conditions under which isotope measurements are informative of environmental exchanges. We find all three isotopic datasets individually contain comparable amounts of information about NEE and LH fluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally, δ 13C provides more information about LH as aridity increases or mean annual precipitation decreases. δ 2H provides more information about LH as temperatures or mean annual precipitation decreases, and also provides more information about NEE as temperatures decrease. Overall, we show that the stable isotope datasets collected by NEON contribute non-trivial amounts of new information about bulk environmental fluxes useful for interpreting biogeochemical and ecohydrological processes at landscape scales. However, the utility of this new information varies with environmental conditions at continental scales. This study provides an approach for quantifying the value adding non-traditional sensing approaches to environmental monitoring sites and the patterns identified here are expected to aid in modeling and data interpretation efforts focused on constraining carbon and water cycles’ mechanisms.

Increased aerosol scattering contributes to the recent monsoon rainfall decrease over the Gangetic Plain

The climate effects of atmospheric aerosols remain highly uncertain. Part of the uncertainty arises from the fact that scattering and absorbing aerosols have distinct or even opposite effects. Thus their relative fraction is critical in determining the overall aerosol climate effect. This study combines observations and global model simulations to demonstrate that changes in the fraction of scattering and absorbing aerosols play an important role in driving the monsoon precipitation decrease over northern India since the 1980s, especially over the Gangetic Basin. Increased aerosol scattering, or decreased aerosol absorption, manifested as a significant increase of aerosol single scattering albedo (SSA), causes strong cooling in the upper atmosphere. This suppresses vertical convection and thus reduces precipitation. Further analysis of the Couple Model Intercomparison Project Phase 6 multi-model-mean historical simulation shows that failing to capture the SSA increase over northern India is likely an important cause of the simulated precipitation trend bias in this area.

Future Implications of Climate Change on Arum palaestinum Boiss: Drought Tolerance, Growth and Production

The objectives of the work were to understand the potential future climate changes in the Mediterranean region, assess the drought tolerance of the black calla lily (Arum palaestinum Boiss.), and investigate the mechanisms associated with its ability to withstand drought conditions. The Shared Socioeconomic Pathways (SSPs) of the Intergovernmental Panel on Climate Change (IPCC) were used to predict future temperature and precipitation changes. Both the SSP2-4.5 and SSP5-8.5 scenarios predicted a general increase in minimum and maximum temperatures and a decrease in precipitation. The projected increase in minimum temperature ranged from 2.95 °C under SSP2-4.5 to 5.67 °C under SSP5-8.5. The projected increase in maximum temperature ranged from 0.69 °C under SSP2-4.5 to 3.34 °C under SSP5-8.5. The projected decrease in precipitation ranged from −1.04 mm/day under SSP2-4.5 to −1.11 mm/day under SSP5-8.5. Results indicated that drought significantly impacted the physiological responses of the black calla lily. As drought increased, the black calla lily showed a reduction in leaf characteristics and non-structural carbohydrates, while proline content and reducing sugar content were increased, enhancing drought tolerance through osmoregulation. The black calla lily tolerates drought at a total ET of up to 50%. It has the potential to adapt to expected climate change through osmoregulation or by building a carbon and nitrogen sink for stress recovery.

Assessment of blue water-green water interchange under extreme warm and dry events across different ecohydrological regions of western Canada

As key drivers of terrestrial ecosystems and food production, green water (GW) and blue water (BW) are interlinked through natural and anthropogenic processes. Their relationship has reportedly been non-stationary, with an anticipated shift from BW to GW in the future. However, these potential shifts are poorly characterized, particularly in response to future extreme warm-dry events. To address this gap, we used a process-based agro-hydrological model to project GW-BW interlinkages and their potential shifts across three different ecohydrological regions – mountainous, crop, and natural lands – in the Nelson River Basin, the largest agricultural basin in Canada and a major breadbasket for the world. Our results indicate dissimilarity in the physical processes that link GW and BW across different ecohydrological regions, and therefore, disproportional projected changes in BW and GW. In mountainous and natural lands, with projected decreases in precipitation in extreme warm-dry years of up to 56% and 46% under SSP126 and SSP585 scenarios, BW decreases by 64% and 42% while GW decreases only by 37% and 29%. The decline in GW is expected to be less because of the availability of evaporative water supplied by legacy soil moisture and groundwater contributions from earlier seasons. This available water to supply evaporative demand represents a delayed overall shift of BW to GW. In crop lands, however, results show a significant decrease of precipitation (45%-55%), BW (up to 42%), and GW (up to 42%), with no remarkable shift of BW to GW in extreme warm-dry years in the future. The projected BW and GW declines in crop lands may impose severe water shortages on local and regional food production. Projected declines in BW from the mountains that serve as source water for downstream irrigation may exacerbate such possible shortages.

Role of the tropical Indian Ocean in orbitally induced mid-Holocene precipitation variation in northwest China

Investigating long-term natural precipitation variability in Northwest China is crucial for our understanding of the recent wetting trend in this region. This study explores the mechanisms of precipitation variations in Northwest China during the mid-Holocene (MH) using the Community Earth System Model (CESM). We emphasize the role of ocean feedbacks in the response of precipitation changes in the summer half-year (April–September; the rainy season of Northwest China) to orbital forcing, via comparing simulation results from the single atmosphere general circulation model (AGCM) and fully coupled atmosphere–ocean general circulation model (AOGCM) of the CESM. The AOGCM simulations reveal a decrease of precipitation in Northwest China during the MH compared to the pre-Industrial (PI) era, matching well with proxy records. The drier MH is reproduced by the AGCM with orbital forcing when the tropical Indian Ocean cooling is taken into account. The tropical Indian Ocean cooling is affected by the decrease of spring insolation, contrasting to Eurasian warming caused by the increase of summer insolation during the MH. The tropical Indian Ocean cooling contributes to a reduction of the upper-level westerlies to the south of the westerly jet axis and hence a northward shift of the westerly jet during the MH relative to the PI, via amplifying the decrease of meridional thermal gradient from the tropical Indian Ocean to northern Eurasia and weakening the strength of the South Asian high. The northward migration of the westerly jet benefits anomalous downward motion and ultimately weakens precipitation in Northwest China during the MH. Therefore, the tropical Indian Ocean plays an important role in orbital forcing of MH precipitation variations in Northwest China. We further infer that abnormal warming of the tropical Indian Ocean is probably a key factor for the recent wetting trend in Northwest China.

Investigating the influences of climate change on drought in a small data-scarce river catchment

Abstract This study focuses on the influences of climate change on drought characteristics in the small, data-scarce Ndembera River catchment in Tanzania, using the Climatic Research Unit (CRU) datasets and six regional climate models (RCMs) of the CORDEX-Africa framework. The RCM simulations were validated against the CRU data, and their optimistic (RCP 2.6) and worst-case (RCP 8.5) scenarios were used in estimating ensemble simulations using the random forest regression. The ensemble simulations were used to compute standardized precipitation evapotranspiration indices, which characterized drought for the near-future, mid-future, and far-future. Finally, patterns and trends of changes in temperature and precipitation were compared to those in drought characteristics. The findings revealed substantial uncertainty in RCMs, although their ensembles fairly reproduced the historical climate in the catchment. Under the optimistic scenario, a slight decrease in precipitation and a small increase in maximum temperature are projected. Consequently, drought intensity is projected to rise just slightly but remain moderate throughout this century. These changes are expected to be more pronounced during the mid-future than the far-future. Under the worst-case scenario, the projected changes are expected to be more pronounced, especially during the end of the century. These sorts of insights are vital for enhancing adaptations to climate change in catchments.

Storylines of Sahel Precipitation Change: Roles of the North Atlantic and Euro‐Mediterranean Temperature

AbstractFuture changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained.

Projection of Long‐Term Climate Change in China Under COVID‐19 Recovery Emission Scenarios

AbstractThe unexpected emergence of the 2019 coronavirus disease (COVID‐19) pandemic resulted in anthropogenic emissions changes, which have significant consequences for the regional and global climate. However, the long‐term impacts of emission reductions and recovery scenarios on regional climate change in China remain unknown. We use the COVID Model Intercomparison Project (CovidMIP) simulations to project climate change in China in the mid‐21st century under four recovery emission pathways in the post‐COVID era. We found that the temporary emission cut during the COVID‐19 lockdown under the 2‐year‐blip scenario has limited long‐term impact on regional climate change. Anthropogenic emissions are reduced under moderate and stringent greening recovery pathways, leading to reductions of 0.17 ± 0.09°C and 0.35 ± 0.10°C in surface air temperature (SAT) relative to the SSP2‐4.5 baseline in China, respectively. Although the reduction of greenhouse gas emissions decreases SAT, the simultaneous enhanced surface solar radiation due to reduced aerosol concentrations partly offsets the GHG‐induced cooling. Additionally, the weakened aerosol‐cloud interactions associated with reduced aerosols increase the national precipitation by 1.30 ± 0.88 mm month−1 (1.63 ± 1.11%) and 1.91 ± 0.60 mm month−1 (2.39 ± 0.77%) in two green scenarios. As a comparison, regional precipitation decreases by 0.40 ± 0.53 mm month−1 (0.50 ± 0.66%) in the fossil‐based recovery scenario. Therefore, long‐term climate change is more dominated by the emission recovery pathways rather than short‐term emission reduction during the pandemic for China.