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Massman in his paper derives an analytical formula for eddy covariance flux corrections, based on equivalent first-order co-spectral transfer functions both at low and high frequencies. At low frequencies flux attenuation due to digital recursive filtering, frequently applied in eddy covariance systems for trend removal, and spectral cut off due to finite averaging time were considered. This comment discusses the form of the transfer function of high-pass digital recursive filter and consequences of block averaging on flux estimate.

We examine the analysis by Lee (1998) of the scalar conservation budget in advective flows. Lee treated the budget in a one-dimensional framework, neglected horizontal derivatives of turbulent quantities and proposed that mean flow advection, simplified to its vertical component, can be used to improve budget closure, when data from only a single tower are available. We conclude: • that the appropriate analysis framework for constructing such budgets is unavoidably two- or three-dimensional because 2D and 3D mean velocity fields always induce streamwise variation in the eddy fluxes of scalars. • that in such flow fields it is generally incorrect to assume that the vertical component of advection w ∂ c /∂z is everywhere much larger than the horizontal component u ∂ c /∂x The vertical component can only provide a good measure of total advective flux divergence in the special circumstance where the tower is located beneath the vertical stagnation streamline of a recirculating flow. By referring to a linear model of scalar transport over a hill we show that the relationship between u ∂ c /∂x and w ∂ c /∂z is entirely dependent on particular flow conditions and that, in general, w ∂ c /∂z cannot even be used to provide a bound on the magnitude of total advection. • that for measurements at heights small compared to the horizontal scale of the advective flow, the horizontal gradient of turbulent flux ∂ u’c’ /∂x can probably be neglected relative to its vertical equivalent ∂ w’c’ /∂z and, • by using simple hydrodynamic models of 2D flows it can be shown that the vertical gradient of mean vertical velocity is approximately constant over tower heights small compared to the horizontal scale of the advective flow. We also comment on the proper choice of coordinate frame for analysis.

Relationship between latex yield of natural rubber trees (Hevea brasiliensis) and climatic variables have been derived. Under humid tropical rainfed conditions, observed latex yields of rubber trees (clone RRII 105) are highly variable from 19.8 to 90.5 g per tree per tap. Maximum temperature experienced during 1 day, 7 days and 30 days prior to the tapping explains about 32%, 42% and 64%, respectively, of the day-to-day variability in latex yields. The multiple regression models to estimate latex yield on a tapping day, with different antecedent weather conditions are developed. The weekly weather conditions associated with optimum yields were maximum temperature of 30.4°C, minimum temperature of 22.8°C, 5.9 h sunshine and 72 mm of rainfall. Any deviation from these meteorological conditions up to 6 months duration may significantly influence latex production. In general, maximum temperature, sunshine duration, vapour pressure deficit and pan evaporation showed significant negative correlation with yield, irrespective of the time period considered. Whereas, significant positive association was observed between rainfall beyond a month period and the latex yield of rubber trees.

The similarity between the diurnal patterns of monthly mean dew-point temperatures for April in Nlamey, West Africa and Patancheru, India is striking The small diurnal varlatxon in vapour pressure relative to the vapour pressure defictt is similar in both locations. Although the error m estimating dally mean vapour pressure deficit from two spot readings of humidity is small xn the example given by Culf et al (1994), in India this is not always so for lndlvmal days (as opposed to monthly means) To illustrate this I have compared dally mean vapour pressures from the automatic weather statxon at the ICRISAT Center (readmgs every 5 mln) with means from spot readings at 07 15 and 14 15 h The difference between the two, expressed as a percentage of the mean dally vapour pressure deficit, was computed between May 1981 and October 1985 for each day in the analysis of Butler (1992) This gave the distribution of the error in calculating mean dally vapour pressure deficit from two spot readings of humidity (Fig 1) Overall, the mean error for the whole period was close to zero, but on individual days the error commonly exceeded 10% ...

Demetriades-Shah et al. (Agric. For. Meteorol., 1992, 58: 193-207) cast doubts on (a) the dependence of crop growth on incident solar radiation under «normal» field conditions; (b) the validity of the relation between crop growth and intercepted radiation; (c) the need to measure interception as distinct from estimating plausible values. A review of relevant field evidence suggests that most of these doubts are insubstantial

Conventional manure storages are an important source of methane (CH4), a potent greenhouse gas. Anaerobic digestion is an alternative manure management practice potentially able to provide environmental benefits, including the reduction of CH4 emissions from slurry storage. This study was conducted at a commercial farm in Ontario where a biodigester system became operational in May 2012. The purpose was to quantify year-round CH4 emissions from a digestate storage tank, examine the relationship between emissions and its driving factors, and compare these results to a similar emissions dataset from untreated manure measured during one year before the biodigester became operational. A micrometeorological mass balance approach was used to measure CH4 fluxes. Total annual CH4 emissions from digestate were 1.0 kg m−3 y−1, which was 85% lower compared to untreated manure. Monthly average volatile solids (VS) mass in the storage tank was 73 ± 24 Mg for digestate and 107 ± 30 Mg for manure, representing a 32% VS reduction in the tank, suggesting that lower emissions were not only due to VS mass reduction after biodigestion and solid-liquid separation. The annual CH4 emissions scaled by VS were 26 g kg−1 VS y−1 for digestate and 76 g kg−1 VS y−1 for manure, suggesting that VS in the digestate were less suitable for CH4 production (less digestible). This was also verified when investigating the relationship between fluxes and its driving factors: VS concentration did not correlate with CH4 emissions per volume for digestate (r = 0.37; p = 0.29), but did for untreated manure (r = 0.95; p = 0.002). However, the correlation of temperature with emission was stronger for digestate than manure at all depths with no lag, especially at 2 m depth (r = 0.98, p

The effects of manure management practices on ammonia (NH3) emissions were evaluated using a micrometeorological technique at four contrasting dairy storage facilities: untreated raw manure slurry (RM), solid-liquid separation with storage of separated liquids (SL), anaerobic digestion of manure and off-farm materials (AD), and anaerobic digestion with solid-liquid separation and storage of the liquid fraction (ADL). Annual average NH3 emissions per surface area were lowest for RM (2.7 g m−2 d−1), followed by SL (4.5 g m−2 d−1), AD (10.0 g m−2 d−1), and ADL (15.5 g m−2 d−1). Lower NH3 emissions from the RM storage were partly due to the 30 cm thick surface crust which formed on the storage surface in summer (wood shavings was used as bedding). Greater surface crusting at the AD storage compared to the ADL storage was also likely the reason for higher emissions at the ADL storage. Relationships between NH3 emissions, temperature, and wind-speed were observed at all sites but were strongest at sites with minimal crusting (SL, ADL) and weak at the RM storage with a crust cover. Total NH3 emissions from each storage facility (kg y−1) did not simply track the differences in fluxes; rather, facilities with greater storage (RM, AD, ADL) had higher emissions than the facility with less storage (SL) due to removal of solids and more frequent field application. Overall, bedding material, manure processing, and storage management all have important effects on NH3 emissions from manure storage.

Highlights • Improvements in ORYZA (v3) were identified and compared to ORYZA2000. • Soil carbon, nitrogen, and temperature dynamic modules were developed. • Algorithms for the effects of environmental stresses on rice growth were improved. • Case studies confirmed successful improvement of the model., The worldwide usage of and increasing citations for ORYZA2000 has established it as a robust and reliable ecophysiological model for predicting the growth and yield of rice in an irrigated lowland ecosystem. Because of its focus on irrigated lowlands, its computation ability is limited to the representation of the effects of the highly dynamic environments of upland, rainfed, and aerobic ecosystems on rice growth and yield. Additional modules and routines to quantify daily variations in soil temperature, carbon, nitrogen, and environmental stresses were then developed and integrated into ORYZA2000 to capture their effects on primary production, assimilate allocation, root growth, and water and nitrogen uptake. The newest version has been renamed “ORYZA version 3 (v3)”. Case studies have shown that the root mean square errors (RMSE) between simulated and measured values for total biomass and yields ranged from 11.2% to 16.6% across experiments in non-drought and drought and/or nitrogen-deficient environments. ORYZA (v3) showed a significant reduction of the RMSE by at least 20%, thereby improving the model’s capability to represent values measured under extreme conditions. It has also been significantly improved in representing the dynamics of soil water and crop leaf nitrogen contents. With an enhanced capability to simulate rice growth and development and predict yield in non-stressed, water-stressed and nitrogen-stressed environments, ORYZA (v3) is a reliable successor of ORYZA2000.

Long-range forecasts of climatic variables are generated by climate centres around the world using global climate models (GCMs). This paper investigates ensemble forecasting of reference crop evapotranspiration (ETo) based on GCM outputs. The Penman-Monteith formula is used to calculate raw forecasts of ETo from GCM forecasts of solar radiation, temperature, wind speed, and vapor pressure. The Bayesian joint probability (BJP) modelling approach is applied to post-process raw monthly forecasts, separately for different lead times (month 1, 2 and 3 ahead). The Schaake shuffle is then employed to link the ensemble members of post-processed forecasts for all lead times to give a temporal structure. Forecasts of seasonal ETo total are obtained by aggregating the monthly forecasts. For comparison purposes, seasonal forecasts are also derived directly by post-processing raw seasonal forecasts without going through the monthly steps. Three case studies are presented for post-processing forecasts from the Australian Community Climate and Earth System Simulator-Seasonal (ACCESS-S1). Both raw forecasts and observations of monthly and seasonal ETo are found to be reasonably normally distributed. The post-processed forecasts of monthly and seasonal ETo are skilful in reference to climatology forecasts and statistically reliable in ensemble spread. The indirect and direct ways of generating forecasts of seasonal ETo total show similar skill and reliability, demonstrating the effectiveness of the Schaake shuffle. In this paper, the proposed post-processing method is evaluated through leave-one-out cross validation. The method can be easily adapted for post-processing raw GCM forecasts in real-time to produce ensemble forecasts of monthly and seasonal ETo.

The immediate effects of asymmetric warming (i.e., day- and night-time warming) on terrestrial ecosystems have been well documented, but the time-lag effects remain poorly understood. In this paper, we investigated the global inter-annual hysteretic responses of vegetation to asymmetric warming over the period of 1982–2013. The net primary production (NPP) was employed as the indicator of vegetation growth, and accumulated monthly average daily maximum temperature and minimum temperature (ATmax and ATmin) were used to reflect the asymmetric warming condition. Additionally, partial correlation analyses were conducted to examine the correlations between NPP and ATmax/ATmin on a monthly scale. Furthermore, the best time lags that ATmax/ATmin had on NPP and the optimal correlations between ATmax/ATmin and NPP were analyzed by time-lag analyses. The results showed that (i) vegetation responded to the asymmetric warming with near 12-month delays at a global scale, and vegetation exhibited larger lags in responding to recent warming over temperature-limited areas or semiarid and subhumid regions compared to other places; (ii) compared with ATmin, ATmax had longer time-lag effects on biomes over mid-high latitudes (45 °N–90 °N, 23 °S–60 °S) and high altitudes (i.e., the Tibetan Plateau and the Brazil Plateau) and smaller delay impacts on biomes in other regions; (iii) with hysteretic impacts considered, ATmax correlated positively with vegetation in temperature-limited areas and negatively in heat-sufficient and water-deficit places, and the reverse was mostly true for ATmin. These phenomena may be associated with the intrinsic differences in the mechanisms that day- and night-time temperatures have on vegetation growth. Our paper gives new insights into the non-uniform responses of the terrestrial ecosystem to asymmetric warming. Looking ahead, terrestrial ecosystem models are highly recommended to incorporate such non-uniform time-lag impacts and distinguished correlations so as to improve their performances in future work.

Terrestrial vegetation plays pivotal roles on land-atmosphere interactions, and even global climate change. However, limited attempts have been taken to elucidate the responses of vegetation activity to weather-related drivers (e.g., droughts, floods). This is especially in China with vast area and changeable meteorological conditions. In this paper, the performance of two satellite-based indices, namely vegetation condition index (VCI) and vegetation health index (VHI) were analyzed to detect the vegetation responses to weather-related variations. The dynamics of vegetation activity in China were further examined for the period 1982–2013. We found widely-distributed vegetation stresses in the entire country for a long period (about an average of two months per year) from the VCI and the VHI. In addition, both the two indices indicated increasing vegetation activities over most parts of China during 1982–2013. However, there is no consensus between the two indices at spatial pattern and regional totals. This discrepancy can be due to the negative-correlation assumption of the VHI between the VCI and bright temperature. However, we found that the relationship between the VCI and temperature could be changeable in different regions, especially in China with complex topography, diverse climate conditions and different vegetation types. The findings of this paper highlight the necessity to account for dominant controls on vegetation growth when using the VCI and the VHI to analyze vegetation activity.

Hoffmann et al. suspected a likely overestimation of carbon (C) sink reported in our paper (Eze et al., 2018) entitled “Upland grasslands in Northern England were atmospheric carbon sinks regardless of management regime”. They attributed this to potential sources of error associated with the estimation of C fluxes from closed-chamber measurements. We have explained why we think that the C sink reported in our paper was not overestimated as suspected by Hoffmann et al. However, we acknowledge the potential for error due to unavoidable operational and data limitations.

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22gCm−2day−1 to 2.38gCm−2day−1.