High-resolution spatiotemporal distribution of precipitation in Iran: a comparative study with three global-precipitation datasets

High-resolution precipitation datasets are used for numerous applications. However, depending on the procedures for obtaining these products, such as number of observations, quality checking, error-correction procedures, and interpolation techniques, they include many uncertainties. Therefore, the accuracy of these products needs to be evaluated over different regions. In this study, the Iranian National Dataset (INDS), a new 1?×?1 km precipitation dataset based on precipitation data of 1,441 quality-controlled stations for the climatic period from 1961 to 2005, was constructed using the digital elevation model, correlation method, and Kriging interpolation procedure. Iran's annual precipitation values at grids and stations were extracted from Climatic Research Unit (CRU) CL 2.0, CRU TS 3.10.01, and WorldClim datasets, and differences between corresponding values in each of the three datasets and INDS were calculated and analyzed. The coefficient of determination (R ²) between the national network stations' data and the CRU CL 2.0, CRU TS 3.10.01, and WorldClim datasets were 0.50, 0.13, and 0.62, respectively. Moreover, R ² values between the grids of each dataset and INDS were 0.51, 0.40, and 0.60, respectively. To determine the global datasets' efficiency for displaying temporal patterns of precipitation, the monthly values gathered from them at 11 stations (as representative of Iran's various precipitation regimes) were compared with the real values at these stations. The results showed that in term of temporal patterns, the concurrences among the three global datasets and the INDS was more acceptable, especially in the case of CRU CL 2.0. In general, it is concluded that the global datasets could be deployed for the primary assessment of the annual precipitation distribution; however, for more precise studies, use of local data is highly recommended.     

1901~2013年GPCC和CRU降水资料在中国大陆的适用性评估

利用1901~2013年中国大陆地区的气象台站实测降水资料,对东英吉利（East Anglia）大学气候研究中心（Climatic Research Unit,CRU）和全球降水气候中心（Global Precipitation Climatology Centre,GPCC）的降水资料分别从季节、年际和年代际尺度上进行了评估。结果表明：1961~2013年CRU与GPCC降水资料均能较准确地描述中国大陆地区的降水特征,且在东部较西部地区、夏季较冬季与站点实测降水情况更为一致。将中国大陆划分为不同区域并在其季节、年际和年代际时间尺度上通过比较降水偏差绝对值的百分比、均方根误差和相关系数等统计量后发现：CRU在青藏高原和其它较大的山脉附近与站点实测降水的差别较大,且年均降水趋势在西北一带的阿尔金山脉、黄土高原、东南地区和长江下游地区,比实测降水的年均趋势小、甚至出现趋势相反的情况。此外,CRU降水的年代际变化趋势也偏小。而GPCC数据不论是降水量还是降水趋势都更接近实际情况。在1901~1961年,通过与65个长期气象观测站点的降水时间序列比较发现,CRU在110°E以西地区与站点观测的降水资料间的差别较大,而GPCC与站点观测资料的吻合较好。最后,利用1961~2013年两套降水资料和站点实测资料分别计算了标准化降水指数（SPI）,简单分析了中国大陆地区的干旱变化,发现GPCC对旱涝的时空变化特征的描述比CRU更接近站点实际观测;并且CRU也没有反映出1997年夏季中国地区出现的严重干旱情况,而GPCC较为准确地反映出了这一干旱事件特征。因此,本文的研究结果认为,就中国大陆地区长时期降水资料而言,GPCC的适用性优于CRU。     

Trends of seasonal maximum and minimum temperatures and precipitation in Southern Brazil for the 1913–2006 period

Long-term variations of monthly average maximum and minimum temperature (TMAX and TMIN) and precipitation records in southern Brazil are investigated for the 1913–2006 period. These variations are carefully analyzed for seasonal and annual indices, taken as regional averages. For this purpose, the serial correlation and trend of the indices are investigated using the run and Mann–Kendall tests. The significant trends are obtained from linear least-square fits. The annual and seasonal TMIN indices show significant warming trends with magnitudes (1.7°C per 100 years for annual index) comparable to those reported by the Intergovernmental Panel on Climate Change, but lower than those found for the southern Brazil in another previous work. Regarding the two other variables, the indices show significant trends only for summer, being a cooling trend of 0.6°C per 100 years for the TMAX and an increasing trend of 93 mm per 100 years over an average summer precipitation of 367 mm. Concerning the decadal analysis, the 1920s present the lowest annual, autumn, and spring TMIN and the 1990s, the highest ones. The 1970s is the decade with the lowest summer TMAX, and the 1940s the decade with the highest one. The driest decade is the 1940s and the wettest, the 1980s.     

Winter weather regimes over the Mediterranean region: their role for the regional climate and projected changes in the twenty-first century

The winter time weather variability over the Mediterranean is studied in relation to the prevailing weather regimes (WRs) over the region. Using daily geopotential heights at 700 hPa from the ECMWF ERA40 Reanalysis Project and Cluster Analysis, four WRs are identified, in increasing order of frequency of occurrence, as cyclonic (22.0 %), zonal (24.8 %), meridional (25.2 %) and anticyclonic (28.0 %). The surface climate, cloud distribution and radiation patterns associated with these winter WRs are deduced from satellite (ISCCP) and other observational (E-OBS, ERA40) datasets. The LMDz atmosphere–ocean regional climate model is able to simulate successfully the same four Mediterranean weather regimes and reproduce the associated surface and atmospheric conditions for the present climate (1961–1990). Both observational- and LMDz-based computations show that the four Mediterranean weather regimes control the region’s weather and climate conditions during winter, exhibiting significant differences between them as for temperature, precipitation, cloudiness and radiation distributions within the region. Projections (2021–2050) of the winter Mediterranean weather and climate are obtained using the LMDz model and analysed in relation to the simulated changes in the four WRs. According to the SRES A1B emission scenario, a significant warming (between 2 and 4 °C) is projected to occur in the region, along with a precipitation decrease by 10–20 % in southern Europe, Mediterranean Sea and North Africa, against a 10 % precipitation increase in northern European areas. The projected changes in temperature and precipitation in the Mediterranean are explained by the model-predicted changes in the frequency of occurrence as well as in the intra-seasonal variability of the regional weather regimes. The anticyclonic configuration is projected to become more recurrent, contributing to the decreased precipitation over most of the basin, while the cyclonic and zonal ones become more sporadic, resulting in more days with below normal precipitation over most of the basin, and on the eastern part of the region, respectively. The changes in frequency and intra-seasonal variability highlights the usefulness of dynamics versus statistical downscaling techniques for climate change studies.     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

Evaluation of regional climate simulations over the CORDEX-EA-II domain using the COSMO-CLM model

The COSMO-CLM (CCLM) model is applied to perform regional climate simulation over the second phase of CORDEX-East Asia (CORDEX-EA-II) domain in this study. Driven by the ERAInterim reanalysis data, the model was integrated from 1988 to 2010 with a high resolution of 0.22°. The model’s ability to reproduce mean climatology and climatic extremes is evaluated based on various aspects. The CCLM model is capable of capturing the basic features of the East Asia climate, including the seasonal mean patterns, interannual variations, annual cycles and climate extreme indices for both surface air temperature and precipitation. Some biases are evident in certain areas and seasons. Warm and wet biases appear in the arid and semi-arid areas over the northwestern and northern parts of the domain. The simulated climate over the Tibetan Plateau is colder and wetter than the observations, while South China, East China, and India are drier. The model biases may be caused by the simulated anticyclonic and cyclonic biases in low-level circulations, the simulated water vapor content biases, and the inadequate physical parameterizations in the CCLM model. A parallel 0.44° simulation is conducted and the comparison results show some added value introduced by the higher resolution 0.22° simulation. As a result, the CCLM model could be an adequate member for the next stage of the CORDEX-EA project, while further studies should be encouraged.     

格点降水资料在中国东部夏季降水变率研究中的适用性

本文使用1951~2010年PREC、CRU、APHRO和GPCC 4种格点降水资料,通过比较其与中国756站点观测降水资料在中国东部(105°E以东)夏季降水变率中的差异,检验和评估了它们的可靠性和适用性。结果表明:中国东部夏季降水变率的前3个主要模态分别是以江淮流域、长江流域和华北与东北南部为核心的经向多中心分布,有明显的年际和年代际变率特征,且干旱特征较洪涝更明显;长江流域夏季降水异常的主周期为3~7 a和20~50 a,而江淮流域和华北地区夏季降水异常的主周期则为准2 a和准10 a。另外,长江与江淮流域和华南地区分别在1970s末和1990s初发生了显著的年代际转变;4种格点降水资料都能很好地再现中国东部夏季降水的时空变率特征,但由于GPCC格点降水资料是基于更多的基站观测和更精细复杂的质量控制方案得到的,因此它具有更高的可靠性。     

Snowpack and runoff response to climate change in Owens Valley and Mono Lake watersheds

Precipitation from the Eastern Sierra Nevada watersheds of Owens Lake and Mono Lake is one of the main water sources for Los Angeles’ over 4 million people, and plays a major role in the ecology of Mono Lake and of these watersheds. We use the Variable Infiltration Capacity (VIC) hydrologic model at daily time scale, forced by climate projections from 16 global climate models under greenhouse gas emissions scenarios B1 and A2, to evaluate likely hydrologic responses in these watersheds for 1950–2099. Comparing climate in the latter half of the 20th Century to projections for 2070–2099, we find that all projections indicate continued temperature increases, by 2–5 °C, but differ on precipitation changes, ranging from ?24 % to +56 %. As a result, the fraction of precipitation falling as rain is projected to increase, from a historical 0.19 to a range of 0.26–0.52 (depending on the GCM and emission scenario), leading to earlier timing of the annual hydrograph’s center, by a range of 9–37 days. Snowpack accumulation depends on temperature and even more strongly on precipitation due to the high elevation of these watersheds (reaching 4,000 m), and projected changes for April 1 snow water equivalent range from ?67 % to +9 %. We characterize the watershed’s hydrologic response using variables integrated in space over the entire simulated area and aggregated in time over 30-year periods. We show that from the complex dynamics acting at fine time scales (seasonal and sub-seasonal) simple dynamics emerge at this multi-year time scale. Of particular interest are the dynamic effects of temperature. Warming anticipates hydrograph timing, by raising the fraction of precipitation falling as rain, reducing the volume of snowmelt, and initiating snowmelt earlier. This timing shift results in the depletion of soil moisture in summer, when potential evapotranspiration is highest. Summer evapotranspiration losses are limited by soil moisture availability, and as a result the watershed’s water balance at the annual and longer scales is insensitive to warming. Mean annual runoff changes at base-of-mountain stations are thus strongly determined by precipitation changes.     

长江流域气候变化高分辨率模拟与RCP4.5情景下的预估

基于长江流域142个气象站1986—2005年月降水和气温数据,评估由MPI-ESM-LR模式驱动的CCLM区域气候模式对长江流域气温和降水的模拟能力,并采用EDCDF法对气温和降水预估数据进行偏差校正。结果表明：该区域气候模式能较好地模拟出长江流域平均气温的季节变化和空间分布特征,但模拟值无论在季节还是年际尺度上均高于观测值。对降水而言,该模式不能较好地模拟出降水的季节分布特征,导致春季、冬季及年模拟值高于观测值,而夏季和秋季模拟值低于观测值。总体而言,该模式对气温的模拟效果相对较好。偏差校正后的预估结果表明：在RCP4.5情景下,长江流域未来（2016—2035年）平均气温相对于基准期（1986—2005年）将升高0.66℃,年降水量将减少2.2%。     

Dry spell characteristics over India based on IMD and APHRODITE datasets

Selected characteristics of dry spells and associated trends over India during the 1951–2007 period is studied using two gridded datasets: the Indian Meteorological Department (IMD) and the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE) datasets. Two precipitation thresholds, 1 and 3 mm, are used to define a dry day (and therefore dry spells) in this study. Comparison of the spatial patterns of the dry spell characteristics (mean number of dry days, mean number of dry spells, mean and maximum duration of dry spells) for the annual and summer monsoon period obtained with both datasets agree overall, except for the northernmost part of India. The number of dry days obtained with APHRODITE is larger for this region compared to IMD, which is consistent with the smaller precipitation for the region in APHRODITE. These differences are also visible in the spatial patterns of mean and maximum dry spell durations. Analysis of field significance associated with trends, at the level of 34 predefined meteorological subdivisions over the mainland, suggests better agreement between the two datasets in positive trends associated with number of dry days for the annual and summer monsoon period, for both thresholds. Important differences between the two datasets are noted in the field significance associated with the negative trends. While negative trends in annual maximum duration of dry spells appear field significant for the desert regions according to both datasets, they are found field significant for two regions (Punjab and South Interior Karnataka) for the monsoon period for both datasets. This study, in addition to providing information on the spatial and temporal patterns associated with dry spell characteristics, also allows identification of regions and characteristics where the two datasets agree/disagree.     

Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990–2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability.     

Cut-off Lows in the South Africa region and their contribution to precipitation

The contribution of Cut-off Lows (CoLs) to precipitation and extreme rainfall frequency in South Africa has been quantified from 402 station records over the period 1979–2006. Firstly, 500 hPa CoL trajectories over Southern Africa and surrounding oceans were determined and their features thoroughly analyzed. In a second step, using daily precipitable water, outgoing long wave radiation data and station rainfall records, an area was defined where the occurrence of CoLs is associated with rainfall over South Africa. CoLs transiting in the 2.5°E–32.5°E/20°S–45°S are more likely to produce precipitation over the country. When 500 hPa CoLs are centered just off the west coast of the country (around 15°E/32.5°S) their impact is substantial in term of daily rainfall intensity and spatial coverage. CoL rainy days have been studied and it is shown that they significantly contribute to precipitation in South Africa, more strongly along the south and east coasts as well as inland, over the transition zone between the summer and winter rainfall domains where they contribute between 25 to more than 35 % of annual accumulation. At the country scale, CoL rainfall is more intense and widespread in spring than during other seasons. Over the analyzed period, a significant trend in annual CoLs’ frequency shows an increase of about 25 %. This increase is mainly realized in spring and in a lesser extent in summer. This trend is accompanied by a significant increase in the frequency of CoL rainy days specifically along the south coast and over the East of the country during the spring–summer period. In parallel, it is shown that from late spring until summer CoLs’ frequency varies significantly accordingly with large scale circulation modes of the Southern Hemisphere such as the Pacific South American pattern (PSA). This positive trend in CoLs’ frequency may be related with the positive trend in the PSA during the spring–summer period over the three last decades.     

Evaluation of 2-m temperature and precipitation products of the Climate Forecast System version 2 over Iran

Using a continuous multi-decadal simulations over the period 1981–2010, subseasonal to seasonal simulations of the Climate Forecast System version 2 (CFSv2) over Iran against the Climatic Research Unit (CRU) dataset are evaluated. CFSv2 shows cold biases over northern hillsides of the Alborz Mountains with the Mediterranean climate and warm biases over northern regions of the Persian Gulf and the Oman Sea with a dry climate. Magnitude of the model bias for 2-m temperature over different regions of Iran varies by season, with the least bias in temperate seasons of spring and autumn, and the largest bias in summer. The model bias decreases as temporal averaging period increases from seasonal to annual. The forecast generally produces dry and wet biases over dry and wet regions of Iran, respectively. In general, 2-m temperature over Iran is better captured than precipitation, but the prediction skill of precipitation is generally high over western Iran. Averaged over Iran, observations indicated that 2-m temperature has been gradually increasing during the studied period, with a rate of approximately 0.5 °C per decade, and the upward trend is well simulated by CFSv2. Averaged over Iran, both observations and simulation results indicated that precipitation has been decreasing in spring, with averaged decreasing trends of 0.8 mm (observed) and 1.7 mm (simulated) per season each year during the period 1981–2010. Observations indicated that the maximum increasing trend of 2-m temperature has occurred over western Iran (nearly 0.7 °C per decade), while the maximum decreasing trend of annual precipitation has occurred over western and parts of southern Iran (nearly 45 to 50 mm per decade).     

Črna Jama as a cold air trap cave within Postojna Cave,Slovenia

?rna Jama is the coldest section of cave within the Postojna Cave System. Mean annual air temperatures at the ?rna Jama 2 site are 5.6 °C (2015) and 5.7 °C (2016), and at the ?rna Jama 3 site 7.1 °C (2015) and 7.2 (2016), whereas the mean external air temperature was 10.3 °C (2015) and 10.0 °C (2016). In Lepe Jame, the passage most heavily visited by tourists, the mean cave-air temperature is 10.7 °C (2014–2017). ?rna Jama exhibits winter and summer temperature regimes. During warm periods (T_cave < T_out), it acts as a cold air trap, exchanging no air with the outside atmosphere. Under such conditions the cave-air temperature shows no short-term diurnal temperature oscillations. Cave-air temperature is significantly stable and affected only by elevation of the groundwater table, which is associated with precipitation. During cold periods (T_cave > T_out), ventilation takes place and dense, cold, outside air sinks into ?rna Jama because of the favourable cave entrance morphology. Recent ?rna Jama air temperature data (2014–2017) indicate a < 0.5 °C higher temperature than that recorded in historical data since 1933. ?rna Jama is the most appropriate place within the Postojna Cave System to study long-term climatic changes. There are hardly any tourist visits to the cave, and human impacts on the cave climate are essentially reduced.     

Latest update of the climatology and changes in the seasonal distribution of precipitation over China

Based on daily precipitation data from 524 meteorological stations in China during the period 1960–2009, the climatology and the temporal changes (trends, interannual, and decadal variations) in the proportion of seasonal precipitation to the total annual precipitation were analyzed on both national and regional scales. Results indicated that (1) for the whole country, the climatology in the seasonal distribution of precipitation showed that the proportion accounted for 55 % in summer (June–August), for around 20 % in both spring (March–May) and autumn (September–November), and around 5 % in winter (December–February). But the spatial features were region-dependent. The primary precipitation regime, “summer–autumn–spring–winter”, was located in central and eastern regions which were north of the Huaihe River, in eastern Tibet, and in western Southwest China. The secondary regime, “summer–spring–autumn–winter”, appeared in the regions south of the Huaihe River, except Jiangnan where spring precipitation dominated, and the southeastern Hainan Island where autumn precipitation prevailed. (2) For the temporal changes on the national scale, first, where the trends were concerned, the proportion of winter precipitation showed a significantly increasing trend, while that of the other three seasons did not show any significant trends. Second, for the interannual variation, the variability in summer was the largest among the four seasons and that in winter was the smallest. Then, on the decadal scale, China experienced a sharp decrease only in the proportion of summer precipitation in 2000. (3) For the temporal changes on the regional scale, all the concerned 11 geographic regions of China underwent increasing trends in the proportion of winter precipitation. For spring, it decreased over the regions south of the Yellow River but increased elsewhere. The trend in the proportion of summer precipitation was generally opposite to that of spring. For autumn, it decreased over the other ten regions except Inner Mongolia with no trend. It is noted that the interannual variability of precipitation seasonality is large over North China, Huanghuai, and Jianghuai; its decadal variability is large over the other regions, especially over those regions south of the Yangtze River.     

Study on Radar Detection of One Stratiform Cloud Precipitation Process in the Central Part of the Tianshan Mountains in China

In order to investigate the physical structure characteristics of the clouds and precipitation over the Tianshan Mountains in summer, the Urumqi Institute of Desert Meteorology of China Meteorological Administration (CMA) carried out an atmospheric detection experiment in Bayanbulak from 1st to 31st August 2012 by means of a wind-profiling radar and a Doppler weather radar. Using the radar observation data, this paper analyzes the dynamic, thermodynamic, radar echo intensity and macro-micro structure characteristics of the 2–3 August precipitation process. The results show that: (1) The radar echo intensity of this rainfall process changes within 5–38 dBZ, and the precipitation cloud system is under the height of 6500 m, with notable 0 °C level echo bright band between 1200 m and 2000 m height. NCEP analysis data shows that the cloud top temperature ranges from ?25 °C to ?32 °C. These indicate the features of typical stratiform cold cloud precipitation. (2) Atmospheric motion during the precipitation process presents the multi-layer structure with wind velocity varying within the range of 3.0–8.0 m/s. The temperature advection is presented with the vertical structure distribution of “cold-warm-cold”, which indicates relative stability of the atmospheric stratification. (3) By retrieving and analyzing the raindrop size distributions below 0 °C level bright band within 600–1200 m height, when the precipitation evolve from early stage to its peak stage, the concentration of the tiny particle zone (D?≤?2.5 mm) changes a little while the concentration of the medium particle zone (2.5?<?D?≤?4 mm) and the concentration of the large particle(D?>?4 mm) increase considerably; but after peak stage the concentration in the medium particle zone and the concentration in the large particle zone decline first, then the concentration in the tiny particle zone reduces. (4) Raindrop size distribution data is used to calculate the precipitation intensity and the liquid water content, whose spatial-temporal variation characteristics are the same. During peak stage of the precipitation, the instantaneous precipitation intensity reaches 5.0 mm/h, and the liquid water content reaches 0.35 g/m³. This study would help deepen the understanding on the physical structure of the clouds and precipitation over the Tianshan Mountains in summer, and also provide some scientific basis for cloud seeding operation over this area.     

An Evaluation of Temperature and Precipitation Surface-Based and Reanalysis Datasets for the Canadian Arctic, 1950–2010

The spatial and temporal consistency of seasonal air temperature and precipitation in eight widely used gridded observation-based climate datasets (CANGRD, CRU-TS3.1, CRUTEM4.1, GISTEMP, GPCC, GPCP, HadCRUT3, and UDEL) and eight reanalyses (20CR, CFSR, ERA-40, ERA-Interim, JRA25, MERRA, NARR, and NCEP2) was evaluated over the Canadian Arctic for the 1950–2010 period. The evaluation used the CANGRD dataset, which is based on homogenized temperature and adjusted precipitation from climate stations, as a reference. Dataset agreement and bias were observed to exhibit important spatial, seasonal, and temporal variability over the Canadian Arctic with the largest spread occurring between datasets over mountain and coastal regions and over the Canadian Arctic Archipelago. Reanalysis datasets were typically warmer and wetter than surface observation-based datasets, with CFSR and 20CR exhibiting biases in total annual precipitation on the order of 300?mm. Warm bias in 20CR exceeded 12°C in winter over the western Arctic. Analysis of the temporal consistency of datasets over the 1950–2010 period showed evidence of discontinuities in several datasets as well as a noticeable increase in dataset spread in the period after approximately 2000. Declining station networks, increased automation, and the inclusion of new satellite data streams in reanalyses are potential contributing factors to this phenomenon. Evaluation of trends over the 1950–2010 period showed a relatively consistent picture of warming and increased precipitation over the Canadian Arctic from all datasets, with CANGRD giving moistening trends two times larger than the multi-dataset average related to the adjustment of the station precipitation data. The study results indicate that considerable care is needed when using gridded climate datasets in local or regional scale applications in the Canadian Arctic.     

Quantitative relationships between precipitation and temperature over Northeast China, 1961–2010

This paper investigates monthly and seasonal precipitation–temperature relationships (PTRs) over Northeast China using a method proposed in this study. The PTRs are influenced by clouds, latent and sensible heat conversion, precipitation type, etc. In summer, the influences of these factors on temperature decrease are different for various altitudes, latitudes, longitudes, and climate types. Stronger negative PTRs ranging from ?0.049 to ?0.075 °C/mm mostly occur in the semi-arid region, where the cold frontal-type precipitation dominates. In contrast, weaker negative PTRs ranging from ?0.004 to ?0.014 °C/mm mainly distribute in Liaoning Province, where rain is mainly orographic rain controlled by the warm and humid air of East Asian summer monsoon. In winter, surface temperature increases owing to the release of latent heat and sensible heat when precipitation occurs. The stronger positive PTRs ranging from 0.963 to 3.786 °C/mm mostly occur at high altitudes and latitudes due to more release of sensible heat. The enhanced atmospheric counter radiation by clouds is the major factor affecting increases of surface temperature in winter and decreases of surface temperature in summer when precipitation occurs.     

北半球环状模和东北冷涡与我国东北夏季降水关系分析

利用国家气象信息中心提供的1951～2004年全国160个测站月平均降水资料和欧洲中心提供的ERA-40再分析资料,对近50多年东北地区夏季降水、东北冷涡与前期北半球环状模和海温的关系进行了统计分析,定义了一个夏季(6～8月)东北冷涡强度指数(NECVI)。结果表明:NECVI能够较好表征东北低涡的气候效应;夏季东北冷涡强度与降水存在显著的正相关,东北冷涡强年,降水偏多,前期2月北半球环状模(NAM)偏弱;东北冷涡偏弱年,降水偏少,前期2月NAM偏强。此外,夏季东北冷涡与前期的中国近海海温存在显著的负相关,前期NAM和中国近海海温的异常可以作为夏季东北冷涡异常的一个前兆信号,进而为东北地区夏季降水异常的预测提供参考依据。     

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations, spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation, both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset.