Global warming without global mean precipitation increase?

Predicting the distribution pattern changes of dye plant habitats caused by climate change

Climate change has accelerated the habitat loss and fragmentation of wildlife. Dye plants of "Fengxiang dyeing" are important indigenous natural resources for traditional printing and dyeing craft in southwest China, is of practical and cultural importance for dozens of ethnic minorities. However, lack of the spatial distribution information of these plants has hampered holistic and efficient conservation management measures. We analyzed the potentially suitable areas of four dye plants (Liquidambar formosana, Strobilanthes cusia, Persicaria tinctoria and Indigofera tinctoria) necessary for "Fengxiang dyeing" based on their geographical distribution sites under different climatic situations using the maximum entropy (MaxEnt) model. The results showed that temperature, precipitation and elevation were the most important factors affecting the suitable geographical areas of the four dye plants. Under the current climate conditions, the overlapping suitable habitat areas of the four plants were mainly in the four southern provinces of China, including Guizhou, Guangxi, Guangdong and Hainan. L. formosana was used as the base plant for combination with the other three plants under the two future climate scenarios (SSP126 and SSP585), and the overlapping suitable habitat areas of the obtained seven combination patterns were considered suitable for potential craft development. Five patterns showed an increase, while two patterns showed a decreasing trend with the increasing carbon emission. The prediction results showed that the overlapping suitable habitat center of the four plants will gradually move to the northeast, indicating that the overlapping suitable habitat area and craft distribution area will be changed. These results provide the basis for understanding the spatial distribution pattern changes of dye plants caused by climate change and establishing measures for protecting and developing printing and dyeing craft.

Significantly wetter or drier future conditions for one to two thirds of the world's population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world's current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century's end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.

Anthropogenic warming induced intensification of summer monsoon frontal precipitation over East Asia

Summer monsoon frontal rainfall in East Asia (EA) is crucial for water resources and flood hazards in densely populated areas. Recent studies have documented the increasing intensity of summer frontal rainfall over recent decades. However, the extent of ongoing climate change on the intensification of the EA frontal precipitation system remains uncertain. Using an objective method for detecting frontal systems, we found a 17 ± 3% increase in observed frontal rainfall intensity during 1958 to 2015. Climate model simulations with and without greenhouse gases suggest that anthropogenic warming plays a key role in the intensification of EA summer frontal precipitation by 5.8% from 1991 to 2015. The analysis highlights that enhanced water vapor convergence and reinforced western North Pacific subtropical High collectively increased moisture transport to the region, resulting in intensified EA frontal precipitation. The results lend support to the anthropogenic warming-induced enhancement of the EA frontal precipitation and its persistence in the future.

High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901-1930, 1931-1960, 1961-1990, and 1991-2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041-2070 and 2071-2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901-1930 to 1991-2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991-2020 to 2071-2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen .

Room temperature bio-engineered multifunctional carbonates for CO2 sequestration and valorization

This contribution reports, for the first time, on an entirely green bio-engineering approach for the biosynthesis of single phase crystalline 1-D nano-scaled calcite CaCO3. This was validated using H2O as the universal solvent and natural extract of Hyphaene thebaica fruit as an effective chelating agent. In this room temperature green process, CaCl2 and CO2 are used as the unique source of Ca and CO3 respectively in view of forming nano-scaled CaCO3 with a significant shape anisotropy and an elevated surface to volume ratio. In terms of novelty, and relatively to the reported scientific and patented literature in relation to the fabrication of CaCO3 by green nano-chemistry, the current cost effective room temperature green process can be singled out as per the following specificities: only water as universal solvent is used, No additional base or acid chemicals for pH control, No additional catalyst, No critical or supercritical CO2 usage conditions, Only natural extract of thebaica as a green effective chelating agent through its phytochemicals and proper enzematic compounds, room Temperature processing, atmospheric pressure processing, Nanoscaled size particles, and Nanoparticles with a significant shape anisotropy (1-D like nanoparticles). Beyond and in addition to the validation of the 1-D synthesis aspect, the bio-engineered CaCO3 exhibited a wide-ranging functionalities in terms of highly reflecting pigment, an effective nanofertilizer as well as a potential binder in cement industry.

A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information

Precipitation data with high accuracy and spatial resolution characteristics play significant roles in the regional hydrological and eco-environmental system applications. Thus, satellite-based precipitation products (SPPs) with high spatial resolution and high accuracy must be developed. This study proposed a multiple-step scheme to improve the global precipitation measurement (GPM) at daily and monthly timescales over the Tibetan Plateau (TP) from 2014 to 2017. First, combined with the geographically weighted regression (GWR) method using geographic and topographic factors, the gamma-distribution mapping and local intensity scaling (GDM-LOCI) method is applied to effectively merge the observed data attributes and the spatial representation of SPPs at the daily scale by correcting precipitation volumes and frequencies. Second, the areal merged precipitation, normalized difference vegetation index (NDVI), and reanalyzed atmospheric data are used to improve the spatial resolution of monthly GPM with a random forest (RF) model that uses the 17 land cover types to establish the local downscaling model windows. The results show that daily merged precipitation can better reflect the spatial and temporal variability of precipitation than can satellite estimates, and the correlation coefficient (R), and critical success index (CSI) increased by 0.12 and 0.26, respectively. In the merged downscaling model, the merged precipitation factor can weaken the negative effect of the other auxiliary predictors due to its spatial autocorrelation in precipitation estimation. Most importantly, by using the land cover types to establish local model windows for the downscaling model, not only the spatial resolution of the GPM product is downscaled to 1 km, but also the spatial structure of the downscaled products is enhanced, with less deviation and a higher spatial correlation. The R, the root mean square error (RMSE) and the relative bias (BIAS) were 0.89, 50.19 mm and 0.57, respectively. This study presents a promising scheme for generating high-quality precipitation data for regional hydrometeorological research in data-scarce regions.

Emergent constraints on future precipitation changes

Future projections of global mean precipitation change (ΔP) based on Earth-system models have larger uncertainties than projections of global mean temperature changes (ΔT)¹. Although many observational constraints on ΔT have been proposed, constraints on ΔP have not been well studied^2-5 and are often complicated by the large influence of aerosols on precipitation⁴. Here we show that the upper bound (95th percentile) of ΔP (2051-2100 minus 1851-1900, percentage of the 1980-2014 mean) is lowered from 6.2 per cent to 5.2-5.7 per cent (minimum-maximum range of sensitivity analyses) under a medium greenhouse gas concentration scenario. Our results come from the Coupled Model Intercomparison Project phase 5 and phase 6 ensembles^6-8, in which ΔP for 2051-2100 is well correlated with the global mean temperature trends during recent decades after 1980 when global anthropogenic aerosol emissions were nearly constant. ΔP is also significantly correlated with the recent past trends in precipitation when we exclude the tropical land areas with few rain-gauge observations. On the basis of these significant correlations and observed trends, the variance of ΔP is reduced by 8-30 per cent. The observationally constrained ranges of ΔP should provide further reliable information for impact assessments.

Response of Precipitation in Tianshan to Global Climate Change Based on the Berkeley Earth and ERA5 Reanalysis Products

Global climate change has readjusted a global-scale precipitation distribution in magnitude and timing. In mountainous areas, meteorological stations and observation data are very limited, making it difficult to accurately understand the response of precipitation to global climate change. Based on ECMWF Reanalysis v5 precipitation products, Berkeley Earth global temperature, and typical atmospheric circulation indexes, we integrated a gradient descent-nonlinear regression downscaling model, cross wavelet transform, and wavelet correlation method to analyze the precipitation response in Tianshan to global climate change. This study provides a high-resolution (90 m × 90 m) precipitation dataset in Tianshan and confirms that global warming, the North Pacific Pattern (NP), the Western Hemisphere Warm Pool (WHWP), and the Atlantic Multidecadal Oscillation (AMO) are related to the humidification of Tianshan over the past 40 years. The precipitation in Tianshan and global temperature have a resonance period of 8–15 months, and the correlation coefficient is above 0.9. In Tianshan, spring precipitation is determined mainly by AMO, North Tropical Atlantic Sea Level Temperature, Pacific Interdecadal Oscillation (PDO), Tropical North Atlantic Index, WHWP, NP, summer by NP, North Atlantic Oscillation, and PDO, autumn by AMO, and winter by Arctic Oscillation. This research can serve the precipitation forecast of Tianshan and help in the understanding of the regional response to global climate change.

Advances in understanding large-scale responses of the water cycle to climate change

Globally, thermodynamics explains an increase in atmospheric water vapor with warming of around 7%/°C near to the surface. In contrast, global precipitation and evaporation are constrained by the Earth's energy balance to increase at ∼2-3%/°C. However, this rate of increase is suppressed by rapid atmospheric adjustments in response to greenhouse gases and absorbing aerosols that directly alter the atmospheric energy budget. Rapid adjustments to forcings, cooling effects from scattering aerosol, and observational uncertainty can explain why observed global precipitation responses are currently difficult to detect but are expected to emerge and accelerate as warming increases and aerosol forcing diminishes. Precipitation increases with warming are expected to be smaller over land than ocean due to limitations on moisture convergence, exacerbated by feedbacks and affected by rapid adjustments. Thermodynamic increases in atmospheric moisture fluxes amplify wet and dry events, driving an intensification of precipitation extremes. The rate of intensification can deviate from a simple thermodynamic response due to in-storm and larger-scale feedback processes, while changes in large-scale dynamics and catchment characteristics further modulate the frequency of flooding in response to precipitation increases. Changes in atmospheric circulation in response to radiative forcing and evolving surface temperature patterns are capable of dominating water cycle changes in some regions. Moreover, the direct impact of human activities on the water cycle through water abstraction, irrigation, and land use change is already a significant component of regional water cycle change and is expected to further increase in importance as water demand grows with global population.