Phenology shift from 1989 to 2008 on the Tibetan Plateau: an analysis with a process-based soil physical model and remote sensing data

Phenology is critical to ecosystem carbon quantification, and yet has not been well modeled considering both aboveground and belowground environmental variables. This is especially true for alpine and pan-arctic regions where soil physical conditions play a significant role in determining the timing of phenology. Here we examine how the spatiotemporal pattern of satellite-derived phenology is related to soil physical conditions simulated with a soil physical model on the Tibetan Plateau for the period 1989–2008. Our results show that spatial patterns and temporal trends of phenology are parallel with the corresponding soil physical conditions for different study periods. On average, 1 °C increase in soil temperature advances the start of growing season (SOS) by 4.6 to 9.9 days among different vegetation types, and postpones the end of growing season (EOS) by 7.3 to 10.5 days. Soil wetting meditates such trends, especially in areas where warming effect is significant. Soil thermal thresholds for SOS and EOS, defined as the daily mean soil temperatures corresponding to the phenological metrics, are spatially clustered, and are closely correlated with mean seasonal temperatures in Spring and Autumn, respectively. This study highlights the importance and feasibility of incorporating spatially explicit soil temperature and moisture information, instead of air temperature and precipitation, into phenology models so as to improve carbon modeling. The method proposed and empirical relations established between phenology and soil physical conditions for Alpine ecosystems on the Tibetan plateau could also be applicable for other cold regions.     

Earth System Model FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model with the physical climate system model

Earth System Models （ESMs） are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 （CMIP5） modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows. The simulated global total terrestrial gross primary production （GPP） is 124.4 PgC yr-I and net pri- mary production （NPP） is 50.9 PgC yr-1. The entire terrestrial carbon pools contain about 2009.9 PgC, comprising 628.2 PgC and 1381.6 PgC in vegetation and soil pools, respectively. Spatially, in the tropics, the seasonal cycle of NPP and net ecosystem production （NEP） exhibits a dipole mode across the equator due to migration of the monsoon rainbelt, while the seasonal cycle is not so significant in Leaf Area Index （LAI）. In the subtropics, especially in the East Asian monsoon region, the seasonal cycle is obvious due to changes in temperature and precipitation from boreal winter to summer. Vegetation productivity in the northern mid-high latitudes is too low, possibly due to low soil moisture there. On the interannual timescale, the terrestrial ecosystem shows a strong response to ENSO. The model- simulated Nifio3.4 index and total terrestrial NEP are both characterized by a broad spectral peak in the range of 2-7 years. Further analysis indicates their correlation coefficient reaches -0.7 when NEP lags the Nifio3.4 index for about 1-2 months.     

成熟森林生态系统土壤有机碳积累:实现碳中和目标的一条重要途径

为了应对全球气候变化带来的挑战,2020年9月中国提出努力争取在2060年前实现碳中和。对此,生态系统固碳被寄予厚望;然而,生态学理论认为,成熟生态系统的碳输入输出趋于平衡,没有碳的净积累,也就没有碳汇功能,而未成熟的生态系统虽有碳的净积累并具有碳汇功能,但自然界任何未成熟生态系统从它建立的时候开始都在不断地向成熟生态系统演替,即任一生态系统演替的最终结果必然是碳输入输出达到平衡状态。由于森林生态系统碳库是陆地生态系统中最大的碳库,所以人们对其在碳中和上的贡献充满期待。本文以森林生态系统为例,分别考虑森林生态系统碳库的生物量碳库和土壤有机碳库,并基于全球最新研究成果,论证了森林生态系统土壤碳库积累过程具有长久的固碳功能,且不违背成熟生态系统碳输入输出趋于平衡的生态学理论,它能为实现碳中和目标做出贡献。     

Impact of moisture flux convergence and soil moisture on precipitation: a case study for the southern United States with implications for the globe

Interactions between soil moisture, evapotranspiration (ET), atmospheric moisture fluxes and precipitation are complex. It is difficult to attribute the variations of one variable to another. In this study, we investigate the influence of atmospheric moisture fluxes and land surface soil moisture on local precipitation, with a focus on the southern United States (U.S.), a region with a strong humidity gradient and intense moisture fluxes. Experiments with the Weather Research and Forecasting model show that the variation of moisture flux convergence (MFC) is more important than that of soil moisture for precipitation variation over the southern U.S. Further analyses decompose the precipitation change into several contributing factors and show that MFC affects precipitation both directly through changing moisture inflow (wet areas) and indirectly by changing the precipitation efficiency (transitional zones). Soil moisture affects precipitation mainly by changing the precipitation efficiency, and secondly through direct surface ET contribution. The greatest soil moisture effects are over transitional zones. MFC is more important for the probability of heavier rainfall; soil moisture has much weaker impact on rainfall probability and its roles are similar for the probability of intermediate-to-heavy rainfall (>10 mm day^?1). Although MFC is more important than soil moisture for precipitation over most regions, the impact of soil moisture could be large over certain transitional regions. At the submonthly time scale, the African Sahel appears to be the only major region where soil moisture has a greater impact than MFC on precipitation. This study provides guidance to understanding and further investigation of the roles of local land surface processes and large-scale circulations on precipitation.     

Using climate impacts indicators to evaluate climate model ensembles: temperature suitability of premium winegrape cultivation in the United States

We explore the potential to improve understanding of the climate system by directly targeting climate model analyses at specific indicators of climate change impact. Using the temperature suitability of premium winegrape cultivation as a climate impacts indicator, we quantify the inter- and intra-ensemble spread in three climate model ensembles: a physically uniform multi-member ensemble consisting of the RegCM3 high-resolution climate model nested within the NCAR CCSM3 global climate model; the multi-model NARCCAP ensemble consisting of single realizations of multiple high-resolution climate models nested within multiple global climate models; and the multi-model CMIP3 ensemble consisting of realizations of multiple global climate models. We find that the temperature suitability for premium winegrape cultivation is substantially reduced throughout the high-value growing areas of California and the Columbia Valley region (eastern Oregon and Washington) in all three ensembles in response to changes in temperature projected for the mid-twenty first century period. The reductions in temperature suitability are driven primarily by projected increases in mean growing season temperature and occurrence of growing season severe hot days. The intra-ensemble spread in the simulated climate change impact is smaller in the single-model ensemble than in the multi-model ensembles, suggesting that the uncertainty arising from internal climate system variability is smaller than the uncertainty arising from climate model formulation. In addition, the intra-ensemble spread is similar in the NARCCAP nested climate model ensemble and the CMIP3 global climate model ensemble, suggesting that the uncertainty arising from the model formulation of fine-scale climate processes is not smaller than the uncertainty arising from the formulation of large-scale climate processes. Correction of climate model biases substantially reduces both the inter- and intra-ensemble spread in projected climate change impact, particularly for the multi-model ensembles, suggesting that—at least for some systems—the projected impacts of climate change could be more robust than the projected climate change. Extension of this impacts-based analysis to a larger suite of impacts indicators will deepen our understanding of future climate change uncertainty by focusing on the climate phenomena that most directly influence natural and human systems.     

长江下游地区汛期暴雨气候特征分析

根据2008-2013年我国暴雨统计结果, 分析了6 a间我国暴雨分布情况、变化趋势和演变规律, 结果表明:我国年降水量分布总体趋势由西北向东南依次递增, 西南地区东部、江汉地区、江淮地区、江南地区及华南地区是每年暴雨的多发区域, 华南南部(尤其是海南)为显著的暴雨多发区, 年暴雨日数常常超过10 d。我国每年平均暴雨日数为217.5 d, 以6-8月为最多; 平均每年出现39次主要暴雨过程次数, 其中8次由热带气旋登陆引起, 约有58%的主要暴雨过程出现在6-8月, 以7月最多; 每年平均出现特大暴雨26站次, 以华南居多, 年最大日降水量介于336.1~614.7 mm之间, 主要出现在6-10月之间。每年遴选出的强度强、范围大、影响显著的10次重大暴雨事件均出现在5-11月, 其中以南方暴雨占多数。

     

基于降雨-径流长期观测的山洪响应特征分析:以美国本土两个小流域为例

以长期降雨-径流观测资料为基础,建立小流域水文单位线以有效描述山洪响应特征,并探究变化环境下的山洪响应特征是否发生改变。单位线在山洪预报中应用广泛,但在气象领域受关注较少。以两个美国小流域（USGS站点02137727-卡托巴河、01572025-斯瓦塔拉河）为研究对象,探讨考虑不同前期降水和致洪降水条件下单位线提取和优化方法,以及前期降水对流域单位线的影响。结果表明不同前期影响雨量下的平均单位线对降水-径流模拟总体效果较好,两个流域1985年以来共16个降雨径流事件模拟的平均纳什模型效率系数分别为0.846和0.940,平均峰值相对误差分别为9.40%和7.47%。前期影响雨量越大,则单位线峰值越高,峰现时间提前。同时考虑前期降水和致洪降水组合的单位线,能更好反映雨洪事件中山洪响应特征,模拟效果进一步提高,对提高山洪概率预报很有意义。通过分析卡托巴河流域33 a单位线的年际变化,发现降水增多和强降水频率增加导致流域山洪响应特征发生明显变化,单位线峰值呈现增加趋势,涨洪历时呈减少趋势,未来山洪灾害风险变大。     

Critical analysis of simulated daily temperature data from the ARPEGE-climate model: application to climate change in the Champagne wine-producing region

As global warming is scientifically and widely accepted, its impacts at regional scales are raising many questions for wine producers. In particular, climate parameters, especially temperature, play a decisive role in vine growth and grape ripening. An overview of expected climate change in terms of bioclimatic indexes (Winkler and Huglin) and thermal extremes in the wine-producing region of Champagne is presented. A variable-grid atmospheric general circulation model, ARPEGE-Climate, with a local zoom at 50 km over the area of interest, is used to investigate potential future changes in thermal extremes and bioclimatic indexes. Changes in daily maximum and minimum temperatures at key stages are discussed for three emission scenarios (B1, A1B, A2) that are currently used in studies of impacts of climate change. Model outputs are analyzed and critically assessed for a control period (1971–2000) and for changes in extreme events in relation to future scenarios, such as a decrease in extreme low temperatures in spring (April) during bud break and an increase in extreme high temperatures in summer, associated with more frequent heat waves during ripening.     

Shear stress results from a mixed spectral finite-difference model: Application to the Askervein Hill project data

Beljaars et al. (1987) developed a model for neutrally stratified boundary-layer flow over roughness changes and topography. It has been discovered that a constant parameter, , was missing in the algebraic-stress closure equations of their paper. This omission also occurred in the MSFD model code but only affects the Askervein Hill shear-stress results for the E-- turbulence closure in Beljaars et al. It also changes the stress results of Karpik (1988), but not his conclusions regarding the robustness of his improved numerical scheme. The present paper demonstrates the effect of the omission of the parameter, , and tests the sensitivity of the model to variations in its value. The new results are compared with the data and model results of Zeman and Jensen (1987).     

Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs

Future changes of terrestrial ecosystems due to changes in atmospheric CO₂ concentration and climate are subject to a large degree of uncertainty, especially for vegetation in the Tropics. Here, we evaluate the natural vegetation response to projected future changes using an improved version of a dynamic vegetation model (CLM-CN-DV) driven with climate change projections from 19 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The simulated equilibrium vegetation distribution under historical climate (1981–2000) has been compared with that under the projected future climate (2081–2100) scenario for Representative Concentration Pathway 8.5 (RCP8.5) to qualitatively assess how natural potential vegetation might change in the future. With one outlier excluded, the ensemble average of vegetation changes corresponding to climates of 18 GCMs shows a poleward shift of forests in northern Eurasia and North America, which is consistent with findings from previous studies. It also shows a general “upgrade” of vegetation type in the Tropics and most of the temperate zones, in the form of deciduous trees and shrubs taking over C3 grass in Europe and broadleaf deciduous trees taking over C4 grasses in Central Africa and the Amazon. LAI and NPP are projected to increase in the high latitudes, southeastern Asia, southeastern North America, and Central Africa. This results from CO₂ fertilization, enhanced water use efficiency, and in the extra-tropics warming. However, both LAI and NPP are projected to decrease in the Amazon due to drought. The competing impacts of climate change and CO₂ fertilization lead to large uncertainties in the projection of future vegetation changes in the Tropics.     

Knowledge extraction from large climatological data sets using a genome-wide analysis approach: application to the 2005 and 2010 Amazon droughts

Today, the volume of data generated in almost all disciplines, particularly in meteorology and climate science, is dramatically increasing. Among the challenges generated by this “data deluge” is the development of efficient knowledge discovery strategies. Here, we show that statistical and computational tools used to analyze large data sets of genome-wide studies can be fruitfully applied to a climatic context. Although not as powerful as some techniques already in use by climatologists, these tools are simple and robust, and can easily be adapted to detect early warning signals for extreme events like droughts or be used to filter large data sets before applying other more advanced and computationally expensive methods. We test this approach in our investigation of the causes of the Amazon droughts of 2005 and 2010. Our results highlight the major role played in these extreme events by the warming of the sea’s surface temperature, mainly in the tropical North Atlantic. Our findings are in agreement with several analyses published in the literature. The main message we convey is that free and open-source data mining and visualization techniques routinely used in genetic studies can be useful in helping scientists to extract knowledge from large climatic data sets, particularly in regions of the world that are vulnerable to climate change but where the availability of technical expertise is critically scarce.     

Application of the Priestley-Taylor evaporation model to assess the influence of soil moisture on the evaporation from a large weighing lysimeter and class a pan

The influence of soil moisture on evaporation from a 6-m grass-covered lysimeter and from Class A pans was assessed for one summer using the -parameter of the Priestley-Taylor evaporation model appropriate for the individual surfaces computed on a daily basis. Net radiation over the pan was estimated from above-grass measurements using a correlation established between the two, using measurements made in the previous two summers. Changes in heat storage of the water were considered in the derivation of for the pan. A unique relationship for the particular conditions of the site was determined between the for the lysimeter and soil moisture, approaching 1.29 at soil moisture near field capacity, but decreasing to as low as 0.5 for dry soil. The corresponding relationship for the pan showed more scatter, but this was improved by using 5-day running means of evaporation and stratifying the data in terms of wind speed to yield a family of curves. Values for at wet soil conditions varied from 1.07 for 100 km day^–1 wind run to 1.17 for 250 km day^–1 wind run. For each curve, values of increased by about 20%; as the soil dried. The relationships may be used to reduce observed Class A pan evaporation to equivalent values for wet-soil conditions and to estimate near-surface soil moisture and actual evapotranspiration for this particular site. Extension of the technique to other areas requires derivation of similar relationships appropriate for those other locations     

Towards an accurate estimate of the entropy production due to radiative processes: Results with a gray atmosphere model

Summary Recently, there have been attempts at computing the various terms in the steady entropy balance of the climate system. Differing numbers have been published. The global atmospheric entropy production rate by absorption and emission of long-wave radiation is one contribution among many others. This paper is mainly devoted to the calculation of just that contribution for the simple model of a gray atmosphere. The inescapable conclusion is that it is substantial, out-weighing, with the unlikely exception of phase changes, all other contributions not explicitly considered here. The results are compared with those in accounts of a similar nature.With 1 Figure