Interannual Standard Deviation Research Articles

Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh

The occurrence of high temperature and heavy rain events during the monsoon season are a major climate risk affecting aquaculture production in Bangladesh. Despite the advances in the seasonal forecasting, the development of operational tools remains a challenge. This work presents the development of a seasonal forecasting approach to predict the number of warm days (NWD) and number of heavy rain days (NHRD) tailored to aquaculture in two locations of Bangladesh (Sylhet and Khulna). The approach is based on the use of meteorological and pond temperature data to generate linear models of the relationship between three-monthly temperature and rainfall statistics and NWD and NHRD, and on the evaluation of the skill of three operational dynamical models from the North American Multi-Model Ensemble (NMME) project. The linear models were used to evaluate the forecasts for two seasons and 1-month lead time: May to July (MJJ), forecast generated in April, and August to October (ASO), forecast generated in July. Differences were observed in the skill of the models predicting maximum temperature and rainfall (Spearman correlation, Root Mean Square Error, Bias statistics, and Willmott’s Index of Agreement,), in addition to NWD and NHRD from linear models, which also vary for the target seasons and location. In general, the models show higher predictive skill for NWD than NHRD, and for Sylhet than in Khulna. Among the three evaluated NMME models, CanSIPSv2 and GFDL-SPEAR exhibit the best performance, they show similar features in terms of error metrics, but CanSIPSv2 presents a lower interannual standard deviation.

EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

Abstract. This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average −0.09 W m−2 with a standard deviation due to interannual variability of 0.25 W m−2, showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∘C with an interannual standard deviation of 0.17 ∘C, exhibiting a small drift of 0.015 ± 0.005 ∘C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∘C, and its transient climate response is estimated at 2.1 ∘C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.05 ∘C with a standard deviation across ensemble members of 0.35 ∘C in the Northern Hemisphere and 1.29 ± 0.02 ∘C with a corresponding standard deviation of 0.05 ∘C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 ∘C above the preindustrial mean. A 0.5 ∘C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∘C.

A Strong Sub‐Thermocline Intrusion of the North Equatorial Subsurface Current Into the Makassar Strait in 2016–2017

AbstractThe poorly resolved pathway of the sub‐thermocline waters (>300 m) within the Makassar Strait, the primary inflow path of the Indonesian Throughflow, is investigated using in situ mooring measurements and Argo profiles at the entrance to the Indonesian Seas. We focus on the strong sub‐thermocline intrusion in the summer of 2016, when significant changes of sub‐thermocline transport thrice as large as the interannual standard deviation occurred. Analysis suggests that the intrusion was drawn from the North Equatorial Subsurface Current (NESC) flowing westward below the North Equatorial Countercurrent, which was composed of a mixture of intermediate‐depth waters from both the North and South Pacific. The anomalously strong NESC to the Makassar sub‐thermocline in the summer 2016 is suggested to be in response to the 2015/2016 extreme El Niño event, forced by the trade wind anomalies over the western‐central Pacific Ocean through westward and downward propagating baroclinic Rossby waves.

Adaptation of Climate Model Projections of Streamflow to Account for Upstream Anthropogenic Impairments

AbstractA statistical procedure is developed to adjust natural streamflows simulated by dynamical models in downstream reaches, to account for anthropogenic impairments to flow that are not considered in the model. The resulting normalized downstream flows are appropriate for use in assessments of future anthropogenically impaired flows in downstream reaches. The normalization is applied to assess the potential effects of climate change on future water availability on the Rio Grande at a gage just above the major storage reservoir on the river. Model‐simulated streamflow values were normalized using a statistical parameterization based on two constants that relate observed and simulated flows over a 50‐year historical baseline period (1964–2013). The first normalization constant is a ratio of the means, and the second constant is the ratio of interannual standard deviations between annual gaged and simulated flows. This procedure forces the gaged and simulated flows to have the same mean and variance over the baseline period. The normalization constants can be kept fixed for future flows, which effectively assumes that upstream water management does not change in the future, or projected management changes can be parameterized by adjusting the constants. At the gage considered in this study, the effect of the normalization is to reduce simulated historical flow values by an average of 72% over an ensemble of simulations, indicative of the large fraction of natural flow diverted from the river upstream from the gage. A weak tendency for declining flow emerges upon averaging over a large ensemble, with tremendous variability among the simulations. By the end of the 21st Century the higher‐emission scenarios show more pronounced declines in streamflow.

Precipitation is the most crucial factor determining the distribution of moso bamboo in Mainland China

Moso bamboo is widespread in natural forests and is cultivated over large areas in China. This study investigated how climate controls its distribution, about which little is known. We collected moso bamboo presence-absence data from 674 sites with long-term climate data in Mainland China. Generalized additive models that included location and climate variables were used to test the effects of these predictors on the species’ occurrence. We identified the best model as the one with the lowest Akaike’s Information Criterion value that contained only statistically significant predictors. We found precipitation, especially the mean (APRE) and interannual standard deviation (SDPRE) of the annual precipitation at each site, rather than temperature, to be the main factors determining the distribution of moso bamboo in Mainland China. In addition, we found that there was a significant power law relationship between the mean and interannual variance of precipitation, which made it possible to make long-term predictions. The SDPRE in climate scenarios of changes in the APRE could then be calculated using the fitted power law relationship. We simulated six climate scenarios, in which the APRE increased/decreased by 25, 50, and 75%. We used the 0.5 and 0.9 probability contour lines of model predictions to represent the suitable and core distributions, respectively, of moso bamboo under each scenario. The current core distribution of moso bamboo in Mainland China predicted by our model agreed with actual observations. Our model suggested that the middle and lower reaches of the Huaihe River Plain in eastern China should be climatically suitable for the growth of moso bamboo; it seems likely that its current absence there has resulted from intensive land use. Our model predicted that changes in APRE can strongly alter the distribution of moso bamboo. Increased APRE would expand the core distribution of moso bamboo into southern Shandong Province and over all of Chongqing and most of Guizhou Provinces, which are areas not currently in the species’ core distribution. Conversely, decreased APRE would shrink the core distribution of moso bamboo to the junction of Anhui, Fujian, Jiangxi, and Zhejiang Provinces. We showed that the current distribution of moso bamboo is mainly determined by annual precipitation rather than temperature. The deviations between the moso distributions predicted by the climate model and the current distribution in some plain areas might have resulted from human activities. Future changes in annual precipitation will probably change the distribution of moso bamboo considerably.

Evaluation of Near-Surface Wind Speed Changes during 1979 to 2011 over China Based on Five Reanalysis Datasets

Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, including National Centers for Environmental Predictions (NCEP)-U.S. Department of Energy (DOE) Reanalysis 2 (NCEP-2), Modern-ERA Retrospective Analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis Project (JRA-55), Interim ECMWF Re-Analysis product (ERA-Interim), and 20th Century Reanalysis (20CR) in reproducing the climatology, interannual variation, and long-term trend of near-surface (10 m above ground) wind speed, for the period of 1979–2011 over continental China are comprehensively evaluated. Compared to the gridded data compiled from meteorological stations, all five reanalysis datasets reasonably reproduce the spatial distribution of the climatology of near-surface wind speed, but underestimate the intensity of the near-surface wind speed in most regions except for Tibetan Plateau where the wind speed is overestimated. All five reanalysis datasets show large weaknesses in reproducing the annual cycle of near-surface wind speed averaged over the continental China. The near-surface wind speed derived from the observations exhibit significant decreasing trends over most parts of continental China during 1979 to 2011. Although the spatial patterns of the linear trends reproduced by reanalysis datasets are close to the observation, the magnitudes are weaker in annual, spring, summer and autumn season. The qualities of all reanalysis datasets are limited in winter. For the interannual variability, except for winter, all five reanalysis datasets reasonably reproduce the interannual standard deviation but with larger amplitude. Quantitative comparison indicates that among the five reanalysis datasets, the MERRA (JRA-55) shows the relatively highest (lowest) skill in terms of the climatology and linear trend. These results call for emergent needs for developing high quality reanalysis data that can be used in wind resource assessment and planning.

Influence of Intraseasonal Oscillation on the Asymmetric Decays of El Niño and La Niña

Warm and cold phases of El Nino–Southern Oscillation (ENSO) exhibit a significant asymmetry in their decay speed. To explore the physical mechanism responsible for this asymmetric decay speed, the asymmetric features of anomalous sea surface temperature (SST) and atmospheric circulation over the tropical Western Pacific (WP) in El Nino and La Nina mature-to-decay phases are analyzed. It is found that the interannual standard deviations of outgoing longwave radiation and 850 hPa zonal wind anomalies over the equatorial WP during El Nino (La Nina) mature-to-decay phases are much stronger (weaker) than the intraseasonal standard deviations. It seems that the weakened (enhanced) intraseasonal oscillation during El Nino (La Nina) tends to favor a stronger (weaker) interannual variation of the atmospheric wind, resulting in asymmetric equatorial WP zonal wind anomalies in El Nino and La Nina decay phases. Numerical experiments demonstrate that such asymmetric zonal wind stress anomalies during El Nino and La Nina decay phases can lead to an asymmetric decay speed of SST anomalies in the central-eastern equatorial Pacific through stimulating different equatorial Kelvin waves. The largest negative anomaly over the Nino3 region caused by the zonal wind stress anomalies during El Nino can be threefold greater than the positive Nino3 SSTA anomalies during La Nina, indicating that the stronger zonal wind stress anomalies over the equatorial WP play an important role in the faster decay speed during El Nino.

Rainfall climatology, variability, and trends in Veranópolis, Rio Grande do Sul, Brazil

ABSTRACT The objective of this study was to characterize the rainfall climatology in Veranópolis, Rio Grande do Sul, Brazil, through analyses of means, variabilities related to El Niño Southern Oscillation (ENSO), and temporal trends, using a 60-year data series (1956-2015). Descriptive statistics of annual, monthly and seasonal rainfall were used to characterize the rainfall climatology. The differences between seasons, and influence of ENSO were evaluated using analysis of variance and the Duncan’s test. Rainfall trends were evaluated by the Mann Kendall test. The local average annual rainfall is 1,683 mm and the average monthly rainfall is 140 mm, varying from 109 (May) to 182 mm (September). The annual rainfall has high interannual (standard deviation of 327 mm), monthly (60-100 mm) and seasonal (124-183 mm) variabilities, which should be considered in non-irrigated agricultural systems using rainfall as the main source of water supply to plants. Although autumn presents lower average rainfall (346 mm) than the other seasons, its average percentages were similar to the total annual rainfall (21-28%), and the rainfalls are well-distributed in the seasons. Differences between ENSO events occurred in the spring; La Niña years showed lower rainfall (385 mm) than El Niño (549 mm) and neutral (481 mm) years. The annual rainfall tended to increase by 6.3 mm per year (p < 0.01), with increases of 2.5 mm in spring and 1.9 mm in winter (p < 0.10) in the period analyzed.

Water level characteristics of Lake Caizi, lower reaches of the Yangtze River during wintering period based on long-term hydrological alteration

湖泊生态水位过程对维持湖泊生态系统结构、过程和功能的完整性具有重要意义，也是当今湖泊科学领域面临的重要科学问题.基于菜子湖湖区水位站（车富岭水位站）1956-2018年日水位资料，采用pettitt突变检验法分析水位的突变性特征.结合年保证率法得到菜子湖车富岭水位站低水位值，并在此基础上分析了低水位发生时间及历时、候鸟越冬期水位变化速率及其生态水位的区间阈值.主要结论有：菜子湖车富岭水位站1956-2018年年均水位无显著突变.菜子湖车富岭水位站低水位发生时间均值为年内的344 d，年际标准差为27 d，低水位的年均历时为69 d，标准差为49 d，有6年（1978、1997、2015-2018年）未发生低水位事件.菜子湖候鸟越冬期水位变化速率的均值为-0.009 m/d，年均值为-0.034~0.009 m/d，日均值为-0.051~0.016 m/d.菜子湖低水位发生时间的区间阈值为332~351 d，历时的区间阈值为33~98 d，变化速率的区间阈值为-0.070~0.020 m/d.加强菜子湖候鸟越冬期湿地生境保护适应性调度试验研究及生态环境监测，为菜子湖输水水位优化控制和菜子湖湿地生态保护提供科学依据.;The ecological water level process of lakes is of great significance for maintaining the integrity of the lake ecosystem structures, processes and functions, and is also an important scientific issue of lake science field nowadays. Based on the daily water level data, pettitt test was used to analyze the abrupt change characteristics of water level of Lake Caizi at Chefuling Station from 1956-2018. The low water level of Lake Caizi at Chefuling Station was obtained using the annual guaranteed frequency method. The starting time and duration of low water level, the water level change rate during the overwintering period of migratory birds and the interval threshold of ecological water level were analyzed accordingly. The main conclusions are as follows:there is no significant abrupt change point in the average annual water level of Lake Caizi at Chefuling Station from 1956 to 2018. The average occurrence time of low water level of Lake Caizi at Chefuling Stations is day 344, with the inter-annual standard deviation 27 days. The average duration time of annual low water level is 69 days with the standard deviation 49 days. Among the calculated 63 years, there are 6 years (1978, 1997, 2015-2018) without low water level event. The average rate of water level change during the overwintering period of migratory birds is -0.009 m/d, the annual average rate is -0.034-0.009 m/d, and the daily average rate is -0.051-0.016 m/d. The interval threshold of the occurrence of low water level of Lake Caizi is 332-351 d, the interval threshold of the duration time is 33-98 d, and the interval threshold of water level change rate is -0.070-0.020 m/d. Strengthening experimental study on habitat adaptation and ecological environment monitoring during the overwintering period of migratory birds to provide scientific basis for water level regulation and wetland protection of the Lake Caizi.

Probabilistic predictions of the Indian monsoon rainfall in below-normal category using NMME global model products

Seasonal predictions of rainfall during the Indian monsoon from the North American Multi-Model Ensemble (NMME) have been used to compute the prediction skill. Observed and model-predicted rainfall have been put in three categories (namely below-normal, above-normal, and near-normal) based on the value of interannual standard deviation. In this study, focus is on the rainfall in below-normal rainfall category over central India. Forecast products were evaluated against the observed rainfall data for the period from 1982 to 2009. Simple model averaging and singular value decomposition (SVD) methods have been used to prepare the multi-model ensemble (MME) predictions. The root mean square error over central India is very less in the MME predictions compared to the member models. The weighted MME scheme using SVD method only marginally improves the skill over the simple MME scheme. The deterministic forecast skill is low (the correlation coefficient is non-significant) over central India even when the SVD scheme is employed. Both parametric and non-parametric methods have been applied to prepare probabilistic forecasts using multi-model ensemble for seasonal prediction. The probabilistic forecasts are reliable and usable as the hit rate is more than the false alarm rate for below-normal rainfall category.

Spatiotemporal Variation Characteristics of Vegetative PUE in China from 2000 to 2015

Vegetative precipitation-use efficiency (PUE) is a key indicator for evaluating the dynamic response of vegetation productivity to the spatiotemporal variation in precipitation. It is also an important indicator for reflecting the relationship between the water and carbon cycles in a vegetation ecosystem. This paper uses data from MODIS Net Primary Production (NPP) and China’s spatial interpolation data for precipitation from 2000 to 2015 to calculate the annual value, multiyear mean value, interannual standard deviation, and interannual linear trend of Chinese terrestrial vegetative PUE over the past 16 years. Based on seven major administrative regions, eleven vegetation types, and four climate zones, we analyzed the spatiotemporal variation characteristics of China’s vegetative PUE. The research results are shown as follows: (1) China’s vegetative PUE shows obvious spatial variation characteristics, and it is relatively stable interannually, with an overall slight increasing trend, especially in Northwest and Southwest China. The vegetative PUE is higher, and its stability is declined in Xinjiang, western Gansu, and the southern Tibetan valley. The vegetative PUE is lower, and its stability is increased in northeastern Tibet and southwestern Qinghai. An increasing trend in vegetative PUE is obvious at the edge of the Tarim Basin, in western Gansu, the southern Tibetan valley, and northwestern Yunnan. (2) There is a significant difference in the PUEs among different vegetation types. The average PUE of Broadleaf Forest is the highest, and the average PUE of Alpine Vegetation is the lowest. The stability of the PUE of Mixed Coniferous and Broadleaf Forest is declined, and the stability of the PUE of Alpine Vegetation is increased. The increasing speed of the PUE of Grass-forb Community is the fastest, and the decreasing speed of the PUE of Swamp is the fastest. (3) There is a significant difference in the PUEs among different vegetation types in the same climate zone, the difference in vegetative PUE in arid and semiarid regions is mainly affected by precipitation, and the difference in vegetative PUE in humid and semihumid regions is mainly affected by soil factors. The PUEs of the same vegetation type are significantly different among climate zones. The average PUE of Cultural Vegetation has the largest difference, the stability of the PUE of Steppe has the largest difference, and the increasing speed of the PUE of Swamp has the largest difference.

Water stable isotope spatio-temporal variability in Antarctica in 1960–2013: observations and simulations from the ECHAM5-wiso atmospheric general circulation model

Abstract. Polar ice core water isotope records are commonly used to infer past changes in Antarctic temperature, motivating an improved understanding and quantification of the temporal relationship between δ18O and temperature. This can be achieved using simulations performed by atmospheric general circulation models equipped with water stable isotopes. Here, we evaluate the skills of the high-resolution water-isotope-enabled atmospheric general circulation model ECHAM5-wiso (the European Centre Hamburg Model) nudged to European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis using simulations covering the period 1960–2013 over the Antarctic continent. We compare model outputs with field data, first with a focus on regional climate variables and second on water stable isotopes, using our updated dataset of water stable isotope measurements from precipitation, snow, and firn–ice core samples. ECHAM5-wiso simulates a large increase in temperature from 1978 to 1979, possibly caused by a discontinuity in the European Reanalyses (ERA) linked to the assimilation of remote sensing data starting in 1979. Although some model–data mismatches are observed, the (precipitation minus evaporation) outputs are found to be realistic products for surface mass balance. A warm model bias over central East Antarctica and a cold model bias over coastal regions explain first-order δ18O model biases by too-strong isotopic depletion on coastal areas and underestimated depletion inland. At the second order, despite these biases, ECHAM5-wiso correctly captures the observed spatial patterns of deuterium excess. The results of model–data comparisons for the inter-annual δ18O standard deviation differ when using precipitation or ice core data. Further studies should explore the importance of deposition and post-deposition processes affecting ice core signals and not resolved in the model. These results build trust in the use of ECHAM5-wiso outputs to investigate the spatial, seasonal, and inter-annual δ18O–temperature relationships. We thus make the first Antarctica-wide synthesis of prior results. First, we show that local spatial or seasonal slopes are not a correct surrogate for inter-annual temporal slopes, leading to the conclusion that the same isotope–temperature slope cannot be applied for the climatic interpretation of Antarctic ice core for all timescales. Finally, we explore the phasing between the seasonal cycles of deuterium excess and δ18O as a source of information on changes in moisture sources affecting the δ18O–temperature relationship. The few available records and ECHAM5-wiso show different phase relationships in coastal, intermediate, and central regions. This work evaluates the use of the ECHAM5-wiso model as a tool for the investigation of water stable isotopes in Antarctic precipitation and calls for extended studies to improve our understanding of such proxies.

Climatology and Interannual Variability of Winter North Pacific Storm Track in CMIP5 Models

We examine the capability of thirteen Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) models in simulating climatology and interannual variability of Winter North Pacific Storm Track (WNPST). It is found that nearly half of the selected models can reproduce the spatial pattern of WNPST climatology. However, the strength and spatial variation of WNPST climatology are weak in most of the models. Most differences among the models are in the northeast of the simulated multi-model ensemble (MME) climatology, while it is more consistent in the south. The MME can reflect not only the center position, but also the strength and spatial distribution of interannual variation of the WNPST amplitude. Except for CNRM-CM5, the interannual standard deviations of simulated WNPST strength and spatial variation in all other models are weak. ACCESS1-3 and CanESM2 have a better capability in simulating the spatial modes of WNPST, while the simulated second and third modes in some models are in opposite order with those in NCEP (National Centers for Environmental Prediction) reanalysis. Only five models and MME can capture “midwinter suppression” feature in their simulations. Compared with NCEP reanalysis, the winter longitude index is larger and latitude index is smaller in most of the models, indicating the simulated storm track is further east and south. CNRM-CM5, MME and CMCC-CM could be used to evaluate interannual variation of strength index, longitude index and latitude index respectively. Nevertheless, only INM-CM4 and CNRM-CM5 can simulate southward drift of WNPST.

Chemical composition and sources of atmospheric aerosols at Djougou (Benin)

In the framework of the INDAAF (International Network to study Deposition and Atmospheric chemistry in AFrica) program, atmospheric aerosols were collected in PM2.5 and PM10 size fractions at Djougou, Benin, in the West Africa, from November, 2005 to October, 2009. Particulate carbon, ionic species, and trace metals were analyzed. Weekly PM2.5 and PM10 total mass concentrations varied between 0.7 and 47.3 µg m−3 and 1.4–148.3 µg m−3, respectively. We grouped the aerosol chemical compounds into four classes: dust, particulate organic matter (POM), elemental carbon (EC), and ions. We studied the annual variation of each class to determine their contribution in the total aerosol mass concentration and finally to investigate their potential emission sources. On an annual basis, the species presented a well-marked seasonality, with the peak of mass concentration for both sizes registered in dry season, 67 ± 2 to 86 ± 9 versus 14 ± 9 to 34 ± 5% in wet season. These values emphasized the seasonality of the emissions and the relative weak interannual standard deviation indicates the low variability of the seasonality. At the seasonal scale, major contributions to the aerosol chemistry in the dry season are: dust (26–59%), POM (30–59%), EC (5–9%), and ions (3–5%), suggesting a predominance of Sahelian and Saharan dust emissions and biomass burning source in this season. In the wet season, POM is predominant, followed by dust, EC, and ions. These results point out the contribution of surrounded biofuel combustion used for cooking and biogenic emissions during the wet season.

Albedo climatology for European land surfaces retrieved from AVHRR data (1990–2014) and its spatial and temporal analysis from green‐up to vegetation senescence

AbstractSatellite‐based, long‐term records of surface albedo characterization that accurately capture spatial and temporal patterns are essential to develop climate models and to monitor the impact of land use changes on the terrestrial energy and water balance. This study presents the first Bidirectional Reflectance Distribution Function (BRDF) and albedo data set derived from the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage reflectance data acquired on board National Oceanic and Atmospheric Administration and Meteorological Operational platforms from 1990 to 2014 over Europe. The objectives of this paper are to describe the data set's surface albedo climatology and anomalies in the visible, near‐infrared, and shortwave broadbands for the growing season months of May to September in order to facilitate utilization of the data by the climate modeling communities. The results demonstrate that the AVHRR BRDF and albedo data have temporal and spatial patterns that are appropriate for the underlying predominant land cover type and accurately reflect the associated climate variation. Visible and near‐infrared broadband albedo anomalies are found to be contrasting in most years, and their spatial distributions depict responses of vegetation to climate events (e.g., heat waves). Visible albedo of crops and near‐infrared albedo of pastures show a higher interannual variation than respective albedos of other snow‐free land covers, while the interannual standard deviations are found to be lower than 0.015. Our findings indicate the importance of taking into account the spectrally distinct variability of surface albedo when analyzing its complex spatiotemporal dynamics in climate‐related research.

Interannual variability of summer monsoon convective and stratiform precipitations in East Asia during 1998–2013

ABSTRACTIn this paper, interannual variations of convective and stratiform precipitations in summer East Asian monsoon (SEAM) dominion are analysed using 16‐year (1998–2013) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rain products (2A25 and 3A25). It is found that the climatological mean patterns of convective and stratiform rain rates (RRs) are generally similar to each other, but the area fraction (AF) of stratiform precipitation can always reach about 80% and dominates in summer East Asia. Nonetheless, convective precipitation produces similar rainfall quantity with stratiform rainfall. For the RR, AF and contribution fraction (CF), their interannual standard deviation and coefficient of variation (CV) spatial patterns are similar between summer convective and stratiform precipitations in East Asia, because of the fact that they are all maybe influenced by SEAM. Differently, only the intensity of interannual variability (i.e. CV) for convective precipitation AF is much greater than that for stratiform precipitation, while CV for both convective and stratiform precipitations RR/CF are similar in most areas. Moreover, the interannual variability of SEAM is more highly correlated with that of RR for convective precipitation than that for stratiform precipitation in most land regions. While the opposite conclusion is found in ocean regions. Generally, the SEAM intensity in the lower troposphere mainly controls interannual variability of the atmospheric stability and both stratiform and convective precipitations RR. Then, atmospheric stability variation further modulates the AF and CF variations of both stratiform and convective precipitations on the interannual scale. This work mainly provides possible evidence of linkage between large‐scale circulation and convective and stratiform precipitations on the interannual scale. It suggests that the possible dominant mechanism should be explored using more observations and together with model simulations in the future work.

Use of GRACE time-variable data and GLDAS-LSM for estimating groundwater storage variability at small basin scales: a case study of the Nzoia River Basin

This study integrates time-variable Gravity Recovery and Climate Experiment (GRACE) gravimetric measurements and Global Land Data Assimilation System (GLDAS) land surface models (LSM) in order to understand the inter-annual variations and groundwater storage changes (GWSC) in the Nzoia River Basin in Kenya, using the water balance equation and parameters. From averaged GRACE and GWSC data, the results showed that over the 10-year period, the basin experienced a groundwater depth gain of 6.38 mm year−1, which is equivalent to aquifer recharge of 298 million cubic metres (mcm) year−1. The deseasonalized groundwater variation analysis gave a net gain in groundwater storage of 6.21 mm year−1 that is equal to a groundwater recharge gain of 290 mcm year−1. The observed results are comparable to the groundwater safe yield of 330 mcm year−1 as estimated by the Water Resource Management Authority in Kenya. Through cross-plotting and analysis with averaged satellite altimetry data and in situ measurements from rainfall and streamflow discharge, the total water storage change (TWSC) and GWSC in the basin were consistent and closely correlated in variation trends. The inter-annual standard deviation of groundwater change was determined as ±0.24 mm year−1, which is equivalent to 85% degree of confidence in the obtained results. The results in this study show that GRACE gravity-variable solutions and GLDAS-LSM provide reliable data sets suitable for the study of small to large basin groundwater storage variations, especially in areas with scarce and sparsely available in situ data.

Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5–L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project, CPC Merged Analysis of Precipitation and the India Meteorological Department precipitation dataset respectively for L3. Despite significant El-Nino Southern Oscillation (ENSO) spring predictability barrier at L3, the ISMR skill score is highest at L3. Further, large scale zonal wind shear (Webster–Yang index) and SST over Nino3.4 region is best at L1 and L0. This implies that predictability aspect of ISMR is controlled by factors other than ENSO and Indian Ocean Dipole. Also, the model error (forecast error) outruns the error acquired by the inadequacies in the initial conditions (predictability error). Thus model deficiency is having more serious consequences as compared to the initial condition error for the seasonal forecast. All the model parameters show the increase in the predictability error as the lead decreases over the equatorial eastern Pacific basin and peaks at L2, then it further decreases. The dynamical consistency of both the forecast and the predictability error among all the variables indicates that these biases are purely systematic in nature and improvement of the physical processes in the CFSv2 may enhance the overall predictability.

Study of Impact of Climatic Variability on the Sea Surface Temperature and Chlorophyll-a Concentration Using Statistical Analysis on Satellite Derived Data for the Arabian Sea

The impact of long term variation in sea surface temperature (SST) on marine biomass using long-term satellite remote sensing data (1985 - 2006) for SST and (1998 – 2010) for chlorophyll-a was studies. This paper presents the result of the analysis conducted on t wenty one year’s data on high-quality satellite derived Sea Surface Temperature, and twelve years SeaWiFS chlorophyll-a data from Arabian Sea. The results are extracted after processing the data using statistical methods for computation of inter-annual seasonal mean and standard deviation. The mean winter data show that region consisting on Gulf of Oman is colder (< 21 o C) than the rest of Arabian Sea. However, annual mean of winter show the intermittent rise and fall in SST in response to El-Nino occurred in 1986, 1988, 1991–1995, 1997-1998, 2002, 2006 and La-Nino events which occurred in 1985, 1986, 1989, 1996, 1999 and 2000 years. The results of SST means for summer and Post-Summer seasons differ from the phenomenon observed in winter and Pre-Summer. During summer, SST of major part of the western Arabian Sea lower to 15 o C under the influence of up-welled water off Somalia and Oman coast. The results of chlorophyll-a concentration depict the increase of concentration with the lowering of SST in the area of northern Arabian Sea. Similar results are obtained for standard deviation computations of both parameters of sea surface during winter monsoon season. Whereas, the results of Standard Deviation for summer monsoon show the minimum values (<1) in the northern and central Arabian Sea and maximum in the alignment of zone under Ekman process. The comparison with temperature confirms the dependency of productivity of the sea surface.

Climatology of free-tropospheric humidity: extension into the SEVIRI era, evaluation and exemplary analysis

Abstract. A new free-tropospheric humidity (FTH) data record is presented. It is based on observations from the Meteosat Visible and Infrared Imager (MVIRI) onboard Meteosat-2–Meteosat-5, as well as Meteosat-7, and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-8 and Meteosat-9 at the water absorption band near 6.3 μm. The data set is available under clear-sky and low-level cloud conditions. With the extension to SEVIRI observations, the data record covers the period 1983–2009 with a spatial resolution of 0.625° × 0.625° and a temporal resolution of 3 h. The FTH is the mean relative humidity (RH) in a broad layer in the free troposphere. The relation between the observed brightness temperature (BT) and the FTH is well established. Previous retrievals are refined by taking into account the relative humidity Jacobians in the training process of the statistical retrieval. The temporal coverage is extended into the SEVIRI period, the homogenization of the BT record is improved, and the full archive is reprocessed using updated regression coefficients. The FTH estimated from the Meteosat observations is compared to the FTH computed from the RH profiles of the Analyzed RadioSoundings Archive (ARSA). An average relative bias of −3.2% and a relative root-mean-square difference (RMSD) of 16.8% are observed. This relative RMSD agrees with the outcome of an analysis of the total uncertainty of the FTH product. The decadal stability of the FTH data record is 0.5 ± 0.45% per decade. As exemplary applications, the interannual standard deviation, the differences on decadal scales, and the linear trend in the FTH data record and in the frequency of occurrence of FTH &lt; 10% (FTHp10) are analyzed per season. Interannual standard deviation maxima and maxima in absolute decadal differences are featured in gradient areas between dry and wet regions, as well as in areas where FTH reaches minima and FTHp10 reaches maxima. An analysis of the FTH linear trends and of the associated uncertainty estimates is achieved to identify possible problems with the data record. Positive trends in FTHp10 are featured in gradient areas between wet and dry regions, in regions where the FTH is minimum, in regions where FTHp10 is maximum, and in regions where differences between FTHp10 averaged over the 2000s and 1990s are negative. However, these positive trends in FTHp10 are associated with maximum standard deviation and are thus hardly significant. This analysis and intercomparisons with other humidity data records are part of the Global Energy and Water Cycle Experiment (GEWEX) Water Vapor Assessment (G-VAP).