Potential impacts of afforestation on climate change and extreme events in Nigeria

Afforestation is usually thought as a good approach to mitigate impacts of warming over a region. This study presents an argument that afforestation may have bigger impacts than originally thought by previous studies. The study investigates the impacts of afforestation on future climate and extreme events in Nigeria, using a regional climate model (RegCM3), forced with global climate model simulations. The impacts of seven afforestation options on the near future (2031–2050, under A1B scenario) climate and the extreme events are investigated. RegCM3 replicates essential features in the present-day (1981–2000) climate and the associated extreme events, and adequately simulates the seasonal variations over the ecological zones in the country. However, the model simulates the seasonal climate better over the northern ecological zones than over the southern ecological zones. The simulated spatial distribution of the extreme events agrees well with the observation, though the magnitude of the simulated events is smaller than the observed. The study shows that afforestation in Nigeria could have both positive and negative future impacts on the climate change and extreme events in the country. While afforestation reduces the projected global warming and enhances rainfall over the afforested area (and over coastal zones), it enhances the warming and reduces the rainfall over the north-eastern part of the country. In addition, the afforestation induces more frequent occurrence of extreme rainfall events (flooding) over the coastal region and more frequent occurrence of heat waves and droughts over the semi-arid region. The positive and negative impacts of the afforestation are not limited to Nigeria; they extend to the neighboring countries. While afforestation lowers the warming and enhances rainfall over Benin Republic, it increases the warming and lowers the rainfall over Niger, Chad and Cameroon. The result of the study has important implication for the ongoing climate change mitigation and adaptation efforts in Nigeria.     

Changes in climate classification and extreme climate indices from a high-resolution future projection in Korea

We investigate the future changes in the climate zone and six extreme temperature indices in Korea, using the 20-km high-resolution atmospheric general circulation model (MRI-AGCM3.1S). The Trewartha and K?ppen climate classification schemes are applied, and four summer-based extreme temperature indices (i.e., summer days, tropical nights, growing degree days, and cooling degree days (CDD) and two winter-based indices (frost days and heating degree days (HDD) are analyzed. To represent significantly the change in threshold indices, the monthly mean bias is corrected in model. The model result reasonably captures the temporal and spatial distribution of the present-day extreme temperatures associated with topography. It was found that in the future climate, the area of the subtropical climate zone in Korea expands northward and increases by 21% under the Trewartha classification scheme and by 35% under the K?ppen classification scheme. The spatial change in extreme climate indices is significantly modulated by geographical characteristics in relation to land-ocean thermal inertia and topographical effects. The change is manifested more in coastal regions than in inland regions, except for that in summer days and HDD. Regions with higher indices in the present climate tend to reveal a larger increase in the future climate. The summer-based indices display an increasing trend, while the winter-based indices show a decreasing trend. The most significant increase is in tropical nights (+452%), whereas the most significant decrease is in HDD (?25%). As an important indicator of energy-saving applications, the changes in HDD and CDD are compared in terms of the frequency and intensity. The future changes in CDD reveal a higher frequency but a lower temperature than those in HDD. The more frequent changes in CDD may be due to a higher and less dispersed occurrence probability of extreme temperatures during the warm season. The greater increase in extreme temperature events during the summer season remains an important implication of projecting future changes in extreme climate events.     

我国降水变化趋势的空间特征

利用2010—2016年5—9月四川省157个国家自动气象站小时降水资料,采用皮尔逊Ⅲ型概率分布模型对四川全省小时降水进行拟合,给出全省超过不同阈值的降水累积概率空间分布;在此基础上,计算最大小时降水量的概率分布及其重现期极值。结果表明：四川盆地西部沿山一带出现降水频次较少,但易发生较大量级的小时降水,攀西地区东部虽是降水高发区,但出现大量级小时降水的可能性小;50 a一遇小时降水高值中心分布在乐山市北部、遂宁市西北部与绵阳交界处以及达州市北部,其极值可达60 mm以上;100 a一遇小时降水极值分布趋势同50 a一遇的基本一致,其极值达70 mm;小时降水的皮尔逊Ⅲ型概率分布模型偏差系数与降水站点的海拔高度呈对数递减关系,决定系数达0.654 5,表明地形高度对四川小时降水分布有一定影响;此外用k均值聚类法可很好地对四川小时降水进行分区。

     

Scale dependency of regional climate modeling of current and future climate extremes in Germany

A 26-year simulation (1980–2005) was performed with the Weather Research and Forecast (WRF) model over the Volta Basin in West Africa. This was to investigate the ability of a climate version of WRF to reproduce present day temperature and precipitation over the Volta Basin. The ERA-Interim reanalysis and one realization of the ECHAM6 global circulation model (GCM) data were dynamically downscaled using two nested domains within the WRF model. The outer domain had a horizontal resolution of 50 km and covered the whole of West Africa while the inner domain had a horizontal resolution of 10 km. It was observed that biases in the respective forcing data were carried over to the RCM, but also the RCM itself contributed to the mean bias of the model. Also, the biases in the 50-km domain were transferred unchanged, especially in the case of temperature, to the 10-km domain, but, for precipitation, the higher-resolution simulations increased the mean bias in some cases. While in general, WRF underestimated temperature in both the outer (mean biases of ?1.6 and ?2.3 K for ERA-Interim and ECHAM6, respectively) and the inner (mean biases of ?0.9 K for the reanalysis and ?1.8 K for the GCM) domains, WRF slightly underestimated precipitation in the coarser domain but overestimated precipitation in the finer domain over the Volta Basin. The performance of the GCM, in general, is good, particularly for temperature with mean bias of ?0.7 K over the outer domain. However, for precipitation, the added value of the RCM cannot be overlooked, especially over the whole West African region on the annual time scale (mean biases of ?3% for WRF and ?8% for ECHAM6). Over the whole Volta Basin and the Soudano-Sahel for the month of April and spring (MAM) rainfall, respectively, mean bias close to 0% was simulated. Biases in the interannual variability in both temperature and precipitation over the basin were smaller in the WRF than the ECHAM6. High spatial pattern correlations between 0.7 and 0.8 were achieved for the autumn precipitation and low spatial correlation in the range of 0.0 and 0.2 for the winter season precipitation over the whole basin and all the three belts over the basin.     

Consistent scale-dependency of future increases in hourly extreme precipitation in two convection-permitting climate models

Climate Dynamics - Convection-permitting models (CPMs) have been proven successful in simulating extreme precipitation statistics. However, when such models are used to study climate change,...     

Multimodel ensemble projections of future climate extreme changes in the Haihe River Basin,China

Exploring the characteristic of the extreme climatic events, especially future projection is considerably important in assessing the impacts of climatic change on hydrology and water resources system. We investigate the future patterns of climate extremes (2001–2099) in the Haihe River Basin (HRB) derived from Coupled General Circulation Model (CGCM) multimodel ensemble projections using the Bayesian Model Average (BMA) approach, under a range of emission scenarios. The extremes are depicted by three extreme temperature indices (i.e., frost days (FD), growing season length (GSL), and T _min >90th percentile (TN90)) and five extreme precipitation indices (i.e., consecutive dry days (CDD), precipitation ≥10 mm (R10), maximum 5-day precipitation total (R5D), precipitation >95th percentile (R95T), and simple daily intensity index (SDII)). The results indicate frost days display negative trend over the HRB in the 21st century, particularly in the southern basin. Moreover, a greater season length and more frequent warm nights are also projected in the basin. The decreasing CDD, together with the increasing R10, R5D, R95T, and SDII in the 21st century indicate that the extreme precipitation events will increase in their intensity and frequency in the basin. Meanwhile, the changes of all eight extremes climate indices under A2 and A1B scenarios are more pronounced than in B1. The results will be of practical significance in mitigation of the detrimental effects of variations of climatic extremes and improve the regional strategy for water resource and eco-environment management, particularly for the HRB characterized by the severe water shortages and fragile ecological environment.     

Assessing future climate changes and extreme indicators in east and south Asia using the RegCM4 regional climate model

This paper assesses future climate changes over East and South Asia using a regional climate model (RegCM4) with a 50?km spatial resolution. To evaluate the model performance, RegCM4 is driven with ??perfect boundary forcing?? from the reanalysis data during 1970?C1999 to simulate the present day climate. The model performs well in reproducing not only the mean climate and seasonality but also most of the chosen indicators of climate extremes. Future climate changes are evaluated based on two experiments driven with boundary forcing from the European-Hamburg general climate model (ECHAM5), one for the present (1970?C1999) and one for the SRES A1B future scenario (2070?C2099). The model predicts an annual temperature increase of about 3°?C5° (smaller over the ocean and larger over the land), and an increase of annual precipitation over most of China north of 30°N and a decrease or little change in the rest of China, India and Indochina. For temperature-related extreme indicators in the future, the model predicts a generally longer growing season, more hot days in summer, and less frost days in winter. For precipitation-related extremes, the number of days with more than 10?mm of rainfall is predicted to increase north of 30°N and decrease in the south, and the maximum five-day rainfall amount and daily intensity will increase across the whole model domain. In addition, the maximum number of consecutive dry days is predicted to increase over most of the model domain, south of 40°N. Most of the Yangtze River Basin in China stands out as ??hotspots?? of extreme precipitation changes, with the strongest increases of daily rain intensity, maximum five-day rain amount, and the number of consecutive dry days, suggesting increased risks of both floods and droughts.     

未来极端降水对气候平均变暖敏感性的蒙特卡罗模拟试验

利用Weibull分布拟合逐日降水的原始分布模式,并基于统计降尺度和蒙特卡罗随机模拟方法,对中国东部区域各站逐日极端降水量在未来气候变暖条件下的响应特征进行统计数值试验.结果表明,在全球变暖背景下,区域平均温度的改变即可导致区域极端降水概率分布特征的变动.从两个典型代表区域的预估结果中可见,长江中下游南部平均降水量对平均温度升高有正响应,模拟得到的区域极端降水概率分布曲线有明显的向右平移,导致大量级的极端降水的再现期缩短即概率增大.山东及渤海湾区域平均降水量对平均温度升高有负响应,模拟得到的区域极端降水概率密度分布尺度参数变小更明显,即方差增大,表现为左右两侧概率密度增加,同样导致大量级的极端降水再现期缩短即概率增大.本文仅考察了气候均值改变条件下,未来区域气候极端值的概率预估的可行性方案.对于未来气候方差的变化并未作试验,但理论上已经证明,未来气候极端值的概率对于气候方差变化的敏感性可能更大.由于目前尚未整卵出考察方差变化的较为完整的实际观测资料,该问题还有待进一步深入研究.     

Analyses of extreme climate events over china based on CMIP5 historical and future simulations

Based on observations and 12 simulations from Coupled Model Intercomparison Project Phase 5 （CMIP5） models, cli- matic extremes and their changes over China in the past and under the future scenarios of three Representative Concentration Pathways （RCPs） are analyzed. In observations, frost days （FD） and low-temperature threshold days （TN10P） show a de- creasing trend, and summer days （SU）, high-temperature threshold days （TX90P）, heavy precipitation days （R20）, and the contribution of heavy precipitation days （P95T） show an increasing trend. Most models are able to simulate the main char- acteristics of most extreme indices. In particular, the mean FD and TX90P are reproduced the best, and the basic trends of FD, TN10P, SU and TX90P are represented. For the FD and SU indexes, most models show good ability in capturing the spatial differences between the mean state of the periods 1986--2005 and 1961-80; however, for other indices, the simulation abilities for spatial disparity are less satisfactory and need to be improved. Under the high emissions scenario of RCP8.5, the century-scale linear changes of the multi-model ensemble （MME） for FD, SU, TN10P, TX90P, R20 and P95T are -46.9, 46.0, -27.1, 175.4, and 2.9 days, and 9.9%, respectively; and the spatial change scope for each index is consistent with the emissions intensity. Due to the complexities of physical process pararneterizations and the limitation of forcing data, great uncertainty still exists with respect to the simulation of climatic extremes.     

气候极值推断的不确定性及其置信区间初步探讨

提出了气候极值推断的不确定性问题.并以中国156个测站为例着重探讨了广义极值(GEV)分布模式的分位数估计的标准误差对气候极值不确定性的重要影响,评估了极值分位数的置信区间及其在地域上的分布特征.数值试验表明,样本容量(n)大小是影响广义极值的分位数标准误差的最主要因素,而随着重现期加长(概率愈小)其分位数的标准误差必然增大,因此,直接影响了置信区间——即估计的可信度.     

Scaling from stand to landscape scale of climate change mitigation by afforestation and forest management: a modeling approach

There is a gap between the increased scientific understanding of carbon pools and fluxes at individual trees/stand and that of forested landscape with complex structures (i.e. variety of species, age classes, site characteristic and management practices). The question about how results generated from a simulated physiologically distinct individual(s)/stands grown at a particular location (scale) can be extrapolated (scaling) across a diverse population in time and space with diverse environments, has been troubling scientists for many years. Scale and scaling present three problems in common: (a) spatial heterogeneity, (b) non-linearity in response and (c) disturbance regimes. Scale, in particular, presents other three problems: (d) threshold scale for processes, (e) dominant processes with scales and (f) emerging properties of the system. Scaling presents problems with (g) feedbacks between plants and environment and (h) plant interactions. The present study proposes a modeling framework linking a process-based model SECRETS – to overcome some of the scale and scaling problems (a, b, c, d and g) – to a C accounting model GORCAM – to integrate the effects of C stock in wood products and from fossil fuel substitution. The capabilities of the modeling framework are tested against three theoretical complex forested landscapes that combine some of the five following scenarios: existing multifunctional forest under (1) actual and (2) changing environmental conditions, and afforestation of an agricultural area with (3) a new multifunctional forest or with (4) a short rotation coppice (poplar) or with (5) an agricultural crop (miscanthus) for bioenergy production. Forest reserves calculations are included for completeness of the landscape C balance and as reference. Results, on the one hand, suggest that the framework is able to simulate C sequestration and stock in ecosystem pools, wood products and fossil fuel substitution of the scenarios under actual environmental conditions. However, comparison of results under changing environmental conditions, against specific plant literature suggest SECRETS formulation must be improved with recent development in photosynthesis, stomatal conductance and N balances. On the other hand, results also suggest that under actual environmental conditions, the optimum landscape scenario to sequester C and avoid fossil emissions to the atmosphere is composed by existing multifunctional forest, reserves and afforestation with short rotation coppice for bioenergy production.     

Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.     

西藏冰湖溃决灾害事件极端气候特征

以1960年以来西藏境内已有记载的27次冰湖溃决灾害事件作为研究对象,基于西藏国家气象站点长时间序列（有效记录至今）日气温和日降雨数据,计算得到16个极端气温指数和6个极端降雨指数。通过主成分变换,提取综合极端气温指数和综合极端降雨指数,并进行历史（10年内对比）极端气候特征对比,获得冰湖溃决灾害发生当年及当月极端气候状态,结果表明西藏冰湖溃决灾害发生期（当年及当月）极端气候特征显著,反映极端气候状态对于激发西藏冰湖溃决灾害发生的重要贡献,具体表现为：(1) 67%（18次）的冰湖溃决事件发生当年综合极端气温指数和综合极端降雨指数均大于前期50%年份的综合极端气候指数,其中,13次灾害发生当年极端气候异常水平超过前期70%年份;(2)已有灾害暴发月份记载的25次冰湖溃决事件中,19次冰湖溃决事件发生当月极端气候指数异常偏高,11次冰湖溃决事件发生当月极端气温和极端降水均大于75%往年同期综合极端气候指数;(3)部分灾害事件如扎日错（1981年6月)、龙纠错溃决（2000年8月）等,灾害发生当年极端气温状态低于往年,而暴发当月综合极端气温指数和综合极端降雨指数均大于历史同期水平,表现为加剧状态;(4)所有冰湖溃决灾害发生当月的综合极端气温指数均高于往年同期指数,表明短历时极端气温事件对高原冰湖溃决灾害形成具有重要影响。