Changes in the variability of global land precipitation

In our warming climate there is a general expectation that the variability of precipitation (P) will increase at daily, monthly and inter-annual timescales. Here we analyse observations of monthly P (1940-2009) over the global land surface using a new theoretical framework that can distinguish changes in global P variance between space and time. We report a near-zero temporal trend in global mean P. Unexpectedly we found a reduction in global land P variance over space and time that was due to a redistribution, where, on average, the dry became wetter while wet became drier. Changes in the P variance were not related to variations in temperature. Instead, the largest changes in P variance were generally found in regions having the largest aerosol emissions. Our results combined with recent modelling studies lead us to speculate that aerosol loading has played a key role in changing the variability of P

Attribution of satellite-observed vegetation trends in a hyper-arid region of the Heihe River basin, Western China

Terrestrial vegetation dynamics are closely influenced by both climate and by both climate and by land use and/or land cover change (LULCC) caused by human activities. Both can change over time in a monotonic way and it can be difficult to separate the effects of climate change from LULCC on vegetation. Here we attempt to attribute trends in the fractional green vegetation cover to climate variability and to human activity in Ejina Region, a hyper-arid landlocked region in northwest China. This region is dominated by extensive deserts with relatively small areas of irrigation located along the major water courses as is typical throughout much of Central Asia. Variations of fractional vegetation cover from 2000 to 2012 were determined using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data with 250 m spatial resolution over 16-day intervals. We found that the fractional vegetation cover in this hyper-arid region is very low but that the mean growing season vegetation cover has increased from 3.4 % in 2000 to 4.5 % in 2012. The largest contribution to the overall greening was due to changes in green vegetation cover of the extensive desert areas with a smaller contribution due to changes in the area of irrigated land. Comprehensive analysis with different precipitation data sources found that the greening of the desert was associated with increases in regional precipitation. We further report that the area of land irrigated each year can be predicted using the runoff gauged 1 year earlier. Taken together, water availability both from precipitation in the desert and runoff inflow for the irrigation agricultural lands can explain at least 52 % of the total variance in regional vegetation cover from 2000 to 2010. The results demonstrate that it is possible to separate the satellite-observed changes in green vegetation cover into components due to climate and human modifications. Such results inform management on the implications for water allocation between oases in the middle and lower reaches and for water management in the Ejina oasis

A general framework for understanding the response of the water cycle to global warming over land and ocean

Climate models project increases in globally averaged atmospheric specific humidity that are close to the Clausius"Clapeyron (CC) value of around 7%K-1 whilst projections for mean annual global precipitation (P) and evaporation (E) are somewhat muted at around 2%K-1. Such global projections are useful summaries but do not provide guidance at local (grid box) scales where impacts occur. To bridge that gap in spatial scale, previous research has shown that the "wet get wetter and dry get drier" relation, Δ(P -E)αP -E, follows CC scaling when the projected changes are averaged over latitudinal zones. Much of the research on projected climate impacts has been based on an implicit assumption that this CC relation also holds at local (grid box) scales but this has not previously been examined. In this paper we find that the simple latitudinal average CC scaling relation does not hold at local (grid box) scales over either ocean or land. This means that in terms of P -E, the climate models do not project that the "wet get wetter and dry get drier" at the local scales that are relevant for agricultural, ecological and hydrologic impacts. In an attempt to develop a simple framework for local-scale analysis we found that the climate model output shows a remarkably close relation to the long-standing Budyko framework of catchment hydrology. We subsequently use the Budyko curve and find that the local-scale changes in P -E projected by climate models are dominated by changes in P while the changes in net irradiance at the surface due to greenhouse forcing are small and only play a minor role in changing the mean annual P -E in the climate model projections. To further understand the apparently small changes in net irradiance we also examine projections of key surface energy balance terms. In terms of global averages, we find that the climate model projections are dominated by changes in only three terms of the surface energy balance: (1) an increase in the incoming long-wave irradiance, and the respective responses (2) in outgoing longwave irradiance and (3) in the evaporative flux, with the latter change being much smaller than the former two terms and mostly restricted to the oceans. The small fraction of the realised surface forcing that is partitioned into E explains why the hydrologic sensitivity (2%K-1) is so much smaller than CC scaling (7%K -1). Much public and scientific perception about changes in the water cycle has been based on the notion that temperature enhances E. That notion is partly true but has proved an unfortunate starting point because it has led to misleading conclusions about the impacts of climate change on the water cycle. A better general understanding of the potential impacts of climate change on water availability that are projected by climate models will surely be gained by starting with the notion that the greater the enhancement of E, the less the surface temperature increase (and vice versa). That latter notion is based on the conservation of energy and is an underlying basis of climate model projections

Effects of Climate Change and Human Activities on Surface Runoff in the Luan River Basin

Quantifying the effects of climate change and human activities on runoff changes is the focus of climate change and hydrological research. This paper presents an integrated method employing the Budyko-based Fu model, hydrological modeling, and climate elasticity approaches to separate the effects of the two driving factors on surface runoff in the Luan River basin, China. The Budyko-based Fu model and the double mass curve method are used to analyze runoff changes during the period 1958~2009. Then two types of hydrological models (the distributed Soil and Water Assessment Tool model and the lumped SIMHYD model) and seven climate elasticity methods (including a nonparametric method and six Budyko-based methods) are applied to estimate the contributions of climate change and human activities to runoff change. The results show that all quantification methods are effective, and the results obtained by the nine methods are generally consistent. During the study period, the effects of climate change on runoff change accounted for 28.3~46.8% while those of human activities contributed with 53.2~71.7%, indicating that both factors have significant effects on the runoff decline in the basin, and that the effects of human activities are relatively stronger than those of climate change

The contribution of reduction in evaporative cooling to higher surface air temperatures during drought

Higher temperatures are usually reported during meteorological drought and there are two prevailing interpretations for this observation. The first is that the increase in temperature (T) causes an increase in evaporation (E) that dries the environment. The second states that the decline in precipitation (P) during drought reduces the available water thereby decreasing E, and in turn the consequent reduction in evaporative cooling causes higher T. To test which of these interpretations is correct, we use climatic data (T, P) and a recently released database (CERES) that includes incoming and outgoing shortwave and longwave surface radiative fluxes to study meteorological drought at four sites (parts of Australia, US, and Brazil), using the Budyko approximation to calculate E. The results support the second interpretation at arid sites. The analysis also showed that increases in T due to drought have a different radiative signature from increases in T due to elevated CO₂.This research was supported by the Australian Research Council (CE11E0098), the National Natural Science Foundation of China (91125018), and the China Scholarship Council (201306210089)

Partitioning the variance between space and time

Here we decompose the space-time variance of near-surface air temperature using monthly observations for the global land surface (excluding Antarctica) from 1901-2000. To do that, we developed a new method for partitioning the total space-time variance, here called the grand variance, into separate spatial and temporal components. The temporal component is, in turn, further partitioned into the variance relating to different time periods and we use monthly data to decompose intra- and inter-annual components of the variance. The results show that the spatial and temporal components of the variance of near-surface air temperature have both, on average, decreased over time primarily because of reductions in the equator-to-pole (northern) temperature gradient, and because in cold regions, winter is generally warming faster than summer. We also found that in most regions, the inter-annual variance in near-surface air temperature has increased

Comparative Production of Bio-Oil from In Situ Catalytic Upgrading of Fast Pyrolysis of Lignocellulosic Biomass

Catalytic upgrading of fast pyrolysis bio-oil from two different types of lignocellulosic biomass was conducted using an H-ZSM-5 catalyst at different temperatures. A fixed-bed pyrolysis reactor has been used to perform in situ catalytic pyrolysis experiments at temperatures of 673, 773, and 873 K, where the catalyst (H-ZSM-5) has been mixed with wood chips or lignin, and the pyrolysis and upgrading processes have been performed simultaneously. The fractionation method has been employed to determine the chemical composition of bio-oil samples after catalytic pyrolysis experiments by gas chromatography with mass spectroscopy (GCMS). Other characterization techniques, e.g., water content, viscosity, elemental analysis, pH, and bomb calorimetry have been used, and the obtained results have been compared with the non-catalytic pyrolysis method. The highest bio-oil yield has been reported for bio-oil obtained from softwood at 873 K for both non-catalytic and catalytic bio-oil samples. The results indicate that the main effect of H-ZSM-5 has been observed on the amount of water and oxygen for all bio-oil samples at three different temperatures, where a significant reduction has been achieved compared to non-catalytic bio-oil samples. In addition, a significant viscosity reduction has been reported compared to non-catalytic bio-oil samples, and less viscous bio-oil samples have been produced by catalytic pyrolysis. Furthermore, the obtained results show that the heating values have been increased for upgraded bio-oil samples compared to non-catalytic bio-oil samples. The GCMS analysis of the catalytic bio-oil samples (H-ZSM-5) indicates that toluene and methanol have shown very similar behavior in extracting bio-oil samples in contrast to non-catalytic experiments. However, methanol performed better for extracting chemicals at a higher temperature

Wavelet-Based Hydrological Time Series Forecasting

These days wavelet analysis is becoming popular for hydrological time series simulation and forecasting. There are, however, a set of key issues influencing the wavelet-aided data preprocessing and modeling practice that need further discussion. This article discusses four key issues related to wavelet analysis: discrepant use of continuous and discrete wavelet methods, choice of mother wavelet, choice of temporal scale, and uncertainty evaluation in wavelet-aided forecasting. The article concludes with a personal reflection on solving the four issues for improving and supplementing relevant wavelet studies, especially wavelet-based artificial intelligence modeling

Global Freshwater availability below normal conditions and population impact under 1.5°C and 2°C stabilization scenarios

Based on the large ensembles of the half a degree additional warming, prognosis, and projected impacts historical, +1.5 and +2 °C experiments, we quantify changes in the magnitude of water availability (i.e., precipitation minus actual evapotranspiration; a function of monthly precipitation flux, latent heat flux, and surface air temperature) below normal conditions (less than median, e.g., 20th percentile water availability). We found that, relative to the historical experiment, water availability below normal conditions of the +1.5 and +2 °C experiments would decrease in the midlatitudes and the tropics, indicating that hydrological drought is likely to increase in warmer worlds. These cause more (less) people in East Asia, Central Europe, South Asia, and Southeast Asia (West Africa and Alaska/Northwest Canada) to be exposed to water shortage. Stabilizing warming at 1.5 °C instead of 2 °C would limit population impact in most of the regions, less effective in Alaska/Northwest Canada, Southeast Asia, and Amazon. Globally, this reduced population impact is ~117 million people

Scenario set-up and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6