Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia(CA)is highly sensitive and vulnerable to changes in precipitation due to global warming,so the projection of precipitation extremes is essential for local climate risk assessment.However,global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes,especially in areas with complex terrain.In this study,we proposed a statistical downscaling(SD)model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models(GCMs)for present and future(2081-2100)periods under two shared socioeconomic pathways(SSP245 and SSP585).The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity(SDII)and maximum 1-day precipitation(RX1DAY)and overestimate the number of wet days(R1MM)and maximum consecutive wet days(CWD)at stations across CA.However,the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations.Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA.In addition,it is skilled in capturing the spatial patterns of the observed precipitation indices.Obviously,SDII and RX1DAY are improved by the SD model,especially in the southeastern mountainous area.Under the intermediate scenario(SSP245),our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation(R95PTOT)of 0.5 mm d^(-1) and 19.7 mm,respectively,over CA at the end of the 21st century(2081-2100)compared to the present values(1995-2014).More pronounced increases in indices R95PTOT,SDII,number of very wet days(R10MM),and RX1DAY are projected under the higher emission scenario(SSP585),particularly in the mountainous southeastern region.The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081-2100.The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

CMIP6 Evaluation and Projection of Precipitation over Northern China:Further Investigation

Based on 20 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),this article explored possible reasons for differences in simulation biases and projected changes in precipitation in northern China among the allmodel ensemble(AMME),“highest-ranked”model ensemble(BMME),and“lowest-ranked”model ensemble(WMME),from the perspective of atmospheric circulations and moisture budgets.The results show that the BMME and AMME reproduce the East Asian winter circulations better than the WMME.Compared with the AMME and WMME,the BMME reduces the overestimation of evaporation,thereby improving the simulation of winter precipitation.The three ensemble simulated biases for the East Asian summer circulations are generally similar,characterized by a stronger zonal pressure gradient between the mid-latitudes of the North Pacific and East Asia and a northward displacement of the East Asian westerly jet.However,the simulated vertical moisture advection is improved in the BMME,contributing to the slightly higher performance of the BMME than the AMME and WMME on summer precipitation in North and Northeast China.Compared to the AMME and WMME,the BMME projects larger increases in precipitation in northern China during both seasons by the end of the 21st century under the Shared Socioeconomic Pathway 5-8.5(SSP5-8.5).One of the reasons is that the increase in evaporation projected by the BMME is larger.The projection of a greater dynamic contribution by the BMME also plays a role.In addition,larger changes in the nonlinear components in the BMME projection contribute to a larger increase in winter precipitation in northern China.

Future changes of cluster high temperature events over China from RegCM4 ensemble under RCP4.5 scenario

Using the daily maximum temperature of the RegCM4 dynamical downscaling from four global climate models under the historical and RCP4.5 simulations,this study firstly identified the cluster high temperature event(CHTE)occurring in China through a simplified objective method,and then projected its change during the 21st century in terms of the CHTE metrics including frequency,duration,extreme intensity,cumulative intensity,maximum influential area,average influential area,and comprehensive intensity.The ensemble projection indicates that all the CHTE metrics tend to increase toward the end of the 21st century on the national scale.Besides,the occurrence of CHTE shows a longer month span during the middle and the end of the 21st century(from April to October)compared to the present(from April to September),accompanied with the peaks of the frequency,duration,and cumulative intensity shifting from the present July ahead to June.Relative to 1986-2005,the projected slight,moderate,and extreme CHTEs increase by 55%,50%,and 50%(58%,43%,and 60%)during 2046-2065(2080-2099),respectively;the projected severe CHTE increases by 11%during 2046-2065 while decreases by 11% during 2080-2099.Spatially,the CHTE frequency,duration,and cumulative intensity are projected to increase in a widespread region.The largest increase appears in southern China for the frequency and in Xinjiang and Southeast China for the duration and cumulative intensity.We further divided China into five sub-regions to examine the regional features of CHTE changes.It is found that in addition to the increase of CHTEs in each single subregion,a pronounced enhancement is also projected for the occurrence of cross-regional CHTEs,particularly for that across more than two subregions.

Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Pro jection Ensembles

We present new variants of Estimation of Distribution Algorithms (EDA) for large-scale continuous optimisation that extend and enhance a recently proposed random projection (RP) ensemble based approach. The main novelty here is to depart from the theory of RPs that require (sub-)Gaussian random matrices for norm-preservation, and instead for the purposes of high-dimensional search we propose to employ random matrices with independent and identically distributed entries drawn from a t-distribution. We analytically show that the implicitly resulting high-dimensional covariance of the search distribution is enlarged as a result. Moreover, the extent of this enlargement is controlled by a single parameter, the degree of freedom. For this reason, in the context of optimisation, such heavy tailed random matrices turn out to be preferable over the previously employed (sub-)Gaussians. Based on this observation, we then propose novel covariance adaptation schemes that are able to adapt the degree of freedom parameter during the search, and give rise to a flexible approach to balance exploration versus exploitation. We perform a thorough experimental study on high-dimensional benchmark functions, and provide statistical analyses that demonstrate the state-of-the-art performance of our approach when compared with existing alternatives in problems with 1000 search variables.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems.In this study,we focus on the major Belt and Road Initiative(BRI)regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C,2°C,and 3°C above pre-industrial levels under the Representative Concentration Pathway(RCP)8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848°(~5 km)globally from EnviDat.The temperature variables include the daily maximum(Tmax),minimum(Tmin)and average(Tmp)surface air temperatures,and the diurnal temperature range(DTR).We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986-2005.The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986-2005.Furthermore,we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C,2°C,and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean.Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions,and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions.The dataset is available at https://doi.org/10.57760/sciencedb.01850.